{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、前言\n",
    "## 1.1 指数增强思路及实践方式\n",
    "根据华泰证券金工研报《指数增强方法汇总及实例》中的论述，从宏观再到微观，指数增强思路可分为`仓位控制`、`行业轮动`、`选股`三个方面。该三方面在实践方式上又都可以统一区分成`“主动”`和`“量化”`两种，越靠近宏观层面主动与量化的差别越小，越靠近微观层面二者差别越大,目前主流公募指数增强产品以量化为主。\n",
    "\n",
    "**仓位控制**：本质上为中长线择时，其目的是预判大盘走势，在上涨时调高仓位，在下跌时降低仓位，调整频率一般在月频至年频。大盘未来走势主要通过在宏观、政策、经济周期等层面进行分析来综合判断，但由于大盘的走势和宏观经济、微观企业、国家政策、国际形势等因素都密切相关，想要准确判断大盘走势难度很大。 \n",
    "\n",
    "**行业轮动**：是利用行业间相对变化趋势从中获利，是介于宏观和微观视角之间的一个研究领域。在一个完整的经济周期中，既有先行行业，又有跟随行业，通过把握经济周期中的行业轮动顺序，在轮动开始前进行配置，在轮动结束后进行调整，可以获取超额收益，实现指数增强的目标。需注意的是，基于行业轮动进行资产配置需要控制投资组合相对基准的行业偏离度，从而达到控制跟踪误差的目的。\n",
    "\n",
    "**选股**：量化选股模型的主流是多因子模型，还有 SmartBeta、基本面量化选股、事件驱动选股等模型，主动方法选股则主要依靠公司研究，选择被错误低估的股票，此外还可以借鉴期货管理策略中的配对交易应用于股票市场进行个股权重配置。根据选股范围的不同，可以分为在基准指数成份股内选股、在成份股以及部分指定股票中选股（例如在中证 800 成份股范围内选股进行沪深 300 指数增强）、在全部 A 股中选股等。三种方式的自由度依次提升，一般在基金合同中会规定采取哪种选股方式，采用全部 A 股\n",
    "当作备选股票池的产品最多。实际上，由于基金公司内部常会有选股范围限制，所以在操作层面上采用第二种股票池的也比较多。\n",
    "\n",
    "**本文就是基于该思想，站在微观视角，采用的`量化多因子指数增强策略`，相比主动方法，该策略的优势在于能科学控制跟踪误差，减少波动。**\n",
    "\n",
    "此外，不论采用以上哪种操作思路，投资管理人都可以考虑配合衍生金融工具或一些其它方式进行增强，如通过打新或借助股指期货、融资融券、期权、可转债等工具进行增强。 \n",
    " \n",
    "## 1.2 指数增强策略的目标\n",
    "`目标`是在对基准指数跟踪的同时获得超额收益，即在控制跟踪误差的前提下获取增强收益，一般认为指数增强型基金的年化跟踪误差需控制在7.75%以内。跟踪误差是跟踪偏离度的标准差，其公式如下：\n",
    "\n",
    "$$TD_{ti}=R_{ti}-R_{tb}$$\n",
    "\n",
    "$$TE=\\frac{1}{n-1}\\sum_{t=1}^{n}\\left [ TD_{ti} -\\frac{1}{n}\\left ( \\sum_{t=1}^{n}TD_{ti} \\right )\\right ]^{2}$$\n",
    "\n",
    "其中$TD_{ti}$为基金跟踪偏离度，是基金净值变化率（$R_{ti}$）与基金的业绩基准同期收益率（$R_{tb}$）之差。\n",
    "\n",
    "## 1.3 本文主要内容  \n",
    "\n",
    "本文先是构建了收益预测模型，通过打分法得到个股的预测收益率，再构建两个量化多因子指数增强策略，一个是`分层抽样策略`，该策略较为简单，只涉及市值和行业两个重要的风险因子，其核心就是使投资组合在两个风险维度上与基准指数保持一致，在市值、行业属性比较相似的若干只股票里优选一只预期收益最高的进行投资，从而获取超额收益；另一个是`多因子线性优化模型`，该策略较为复杂，其核心是控制投资组合在更多的风险因子上的暴露与基准指数一致，以期获得更小的跟踪误差，该策略一般需要通过求解二次优化或非线性优化问题来构建最优组合。            \n",
    "\n",
    "本文参考：\n",
    "+ 华泰证券《指数增强方法汇总及实例——量化多因子指数增强策略实证》\n",
    "+ 天风证券《基于自适应风险控制的指数增强策略》"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jqdata import *\n",
    "from jqfactor import (get_factor_values,\n",
    "                      calc_factors,\n",
    "                      Factor)\n",
    "\n",
    "import talib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import statsmodels.api as sm\n",
    "from scipy.optimize import minimize\n",
    " \n",
    "from tqdm import tqdm_notebook\n",
    "from dateutil.parser import parse\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、数据筛选\n",
    "\n",
    "1. 回测区间:2014年01月01日至2020年09月30日;\n",
    "2. 调仓日：每月最后一个交易日为调仓日，以每月最后一个交易日的收盘价买入卖出；\n",
    "3. 股票池：HS300；剔除选股日的ST股票；剔除上市不满1年的股票；剔除选股日由于停牌等原因而无法买入的股票；\n",
    "4. 所需因子如下表：\n",
    "\n",
    "|类别|因子名称|因子code|因子计算方式|\n",
    "|--|--|--|\n",
    "|规模|对数市值|natural_log_of_market_cap|总市值取对数|\n",
    "|估值|账面市值比|book_to_price_ratio|净资产/总市值|\n",
    "|估值|市盈率倒数 TTM|EPTTM|归母净利润 TTM/总市值(Factor)|\n",
    "|估值|市销率倒数 TTM|SPTTM|营业收入 TTM/总市值(Factor)|\n",
    "|技术指标|一个月反转|ROC20|过去 20 个交易日涨跌幅|\n",
    "|技术指标|三个月反转|ROC60|过去 60 个交易日涨跌幅|\n",
    "|成长|净利润增长率|net_profit_growth_rate|(今年净利润（TTM）/去年净利润（TTM）)-1(修改)|\n",
    "|成长|营业收入增长率|operating_revenue_growth_rate|今年营业收入（TTM）/去年营业收入（TTM））-1(修改)|\n",
    "|成长|利润总额增长率|total_profit_growth_rate|(今年利润总额（TTM）/去年利润总额（TTM）)-1(修改)|\n",
    "|成长|标准化预期外盈利|SUE|（单季度实际净利润-预期净利润）/预期净利润标准差(Factor)|\n",
    "|成长|标准化预期外收入|SUR|（单季度实际营业收入-预期营业收入）/预期营业收入标准差(Factor)|\n",
    "|盈利|单季度净资产收益率|roe_ttm|单季度归母净利润*2/（期初归母净资产+期末归母净资产)(修改)|\n",
    "|盈利|单季度总资产收益率|roa_ttm|单季度息税前利润*2/（期初总资产+期末总资产）(修改)|\n",
    "|盈利|单季度净资产收益率同比变化|DELTAROE|单季度净资产收益率-去年同期单季度净资产收益率(Factor)|\n",
    "|盈利|单季度总资产收益率同比变化|DELTAROA|单季度总资产收益率-去年同期单季度中资产收益率(Factor)|\n",
    "|流动性|非流动性冲击|ILLIQ|过去20个交易日涨幅绝对值/成交额均值(Factor)|\n",
    "|流动性|一个月日均换手|VOL20|过去20个交易换手率均值|\n",
    "|流动性|三个月日均换手|VOL60|过去60个交易换手率均值|\n",
    "|波动|特异度|IVR|1-过去20交易日Fama-French三因子回归拟合度(Factor)|\n",
    "|波动|一个月真实波动率|ATR1M|过去20个交易日日内真实波幅均值(Factor)|\n",
    "|波动|三个月真实波动率|ATR3M|过去60个交易日日内真实波幅均值(Factor)|\n",
    "\n",
    "*一致性预期、分红数据聚宽没有故模型中也缺失相应的因子*\n",
    "\n",
    "因子计算方式Factor标记为使用Factor类构造的,修改标记为在原始模型上做的修改"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1因子构造\n",
    "\n",
    "部分因子无法直接使用聚宽的因子库获取故这里手动构造模型所需因子，此部分因子在上述模型所需的表中的因子计算方式项中标记有Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "class EPTTM(Factor):\n",
    "    \n",
    "    name = 'EPTTM'\n",
    "    max_window = 1\n",
    "    dependencies = ['pe_ratio']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        return (1 / data['pe_ratio']).iloc[0]\n",
    "    \n",
    "class SPTTM(Factor):\n",
    "    \n",
    "    name = 'SPTTM'\n",
    "    max_window = 1\n",
    "    dependencies = ['ps_ratio']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        return (1 / data['ps_ratio']).iloc[0]\n",
    "    \n",
    "\n",
    "class SUE0(Factor):\n",
    "\n",
    "    '''含漂移项'''\n",
    "\n",
    "    name = 'SUE0'\n",
    "    max_window = 1\n",
    "\n",
    "    global fields\n",
    "\n",
    "    fields = [f'net_profit_{i}' if i != 0 else 'net_profit' for i in range(9)]\n",
    "\n",
    "    dependencies = fields\n",
    "\n",
    "    def calc(self, data):\n",
    "\n",
    "        # 数据结构为 columns为 net_profit至net_profit_8\n",
    "        df = pd.concat([v.T for v in data.values()], axis=1)\n",
    "        df.columns = fields\n",
    "        df.fillna(0, inplace=True)\n",
    "\n",
    "        # 漂移项可以根据过去两年盈利同比变化Q{i,t} - Q{i,t-4}的均值估计\n",
    "        # 数据结构为array\n",
    "        tmp = df.iloc[:, 1:5].values - df.iloc[:, 5:].values\n",
    "\n",
    "        C = np.mean(tmp, axis=1)  # 漂移项 array\n",
    "\n",
    "        epsilon = np.std(tmp, axis=1)  # 残差项epsilon array\n",
    "\n",
    "        Q = df.iloc[:, 4] + C + epsilon  # 带漂移项的季节性随机游走模型\n",
    "\n",
    "        return (df.iloc[:, 0] - Q) / epsilon\n",
    "    \n",
    "class SUR0(Factor):\n",
    "\n",
    "    '''含漂移项'''\n",
    "\n",
    "    name = 'SUR0'\n",
    "    max_window = 1\n",
    "\n",
    "    global fields\n",
    "\n",
    "    fields = [f'operating_revenue_{i}' if i !=\n",
    "              0 else 'operating_revenue' for i in range(9)]\n",
    "\n",
    "    dependencies = fields\n",
    "\n",
    "    def calc(self, data):\n",
    "\n",
    "        # 数据结构为 columns为 net_profit至net_profit_8\n",
    "        df = pd.concat([v.T for v in data.values()], axis=1)\n",
    "        df.columns = fields\n",
    "        df.fillna(0, inplace=True)\n",
    "\n",
    "        # 漂移项可以根据过去两年盈利同比变化Q{i,t} - Q{i,t-4}的均值估计\n",
    "        # 数据结构为array\n",
    "        tmp = df.iloc[:, 1:5].values - df.iloc[:, 5:].values\n",
    "\n",
    "        C = np.mean(tmp, axis=1)  # 漂移项 array\n",
    "\n",
    "        epsilon = np.std(tmp, axis=1)  # 残差项epsilon array\n",
    "\n",
    "        Q = df.iloc[:, 4] + C + epsilon  # 带漂移项的季节性随机游走模型\n",
    "\n",
    "        return (df.iloc[:, 0] - Q) / epsilon\n",
    "    \n",
    "class DELTAROE(Factor):\n",
    "    \n",
    "    '''单季度净资产收益率-去年同期单季度净资产收益率'''\n",
    "    \n",
    "    name = 'DELTAROE'\n",
    "    max_window = 1\n",
    "    dependencies = ['roe','roe_4']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        return (data['roe'] - data['roe_4']).iloc[0]\n",
    "    \n",
    "class DELTAROA(Factor):\n",
    "    \n",
    "    '''单季度总资产收益率-去年同期单季度中资产收益率'''\n",
    "    \n",
    "    name = 'DELTAROA'\n",
    "    max_window = 1\n",
    "    dependencies = ['roa','roa_4']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        \n",
    "        return (data['roa'] - data['roa_4']).iloc[0]\n",
    "    \n",
    "class ILLIQ(Factor):\n",
    "    \n",
    "    name = 'ILLIQ'\n",
    "    max_window = 21\n",
    "    dependencies = ['close','money']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        abs_ret = np.abs(data['close'].pct_change().shift(1).iloc[1:])\n",
    "        \n",
    "        return (abs_ret / data['money'].iloc[1:]).mean()\n",
    "    \n",
    "class ATR1M(Factor):\n",
    "    \n",
    "    '''过去20个交易日日内真实波幅均值'''\n",
    "    name = 'ATR1M'\n",
    "    max_window = 22\n",
    "    dependencies = ['close','high','low']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        HIGH = data['high'].shift(1).iloc[1:]\n",
    "        LOW = data['low'].shift(1).iloc[1:]\n",
    "        CLOSE = data['close'].shift(1).iloc[1:]\n",
    "        \n",
    "        tmp = np.maximum(HIGH - LOW,np.abs(CLOSE.shift(1) - HIGH))\n",
    "        TR = np.maximum(tmp,np.abs(CLOSE.shift(1) - LOW))\n",
    "    \n",
    "        return TR.iloc[-20:].mean()\n",
    "    \n",
    "class ATR3M(Factor):\n",
    "    \n",
    "    '''过去60个交易日日内真实波幅均值'''\n",
    "    name = 'ATR3M'\n",
    "    max_window = 62\n",
    "    dependencies = ['close','high','low']\n",
    "    \n",
    "    def calc(self,data):\n",
    "        \n",
    "        HIGH = data['high'].shift(1).iloc[1:]\n",
    "        LOW = data['low'].shift(1).iloc[1:]\n",
    "        CLOSE = data['close'].shift(1).iloc[1:]\n",
    "        \n",
    "        tmp = np.maximum(HIGH - LOW,np.abs(CLOSE.shift(1) - HIGH))\n",
    "        TR = np.maximum(tmp,np.abs(CLOSE.shift(1) - LOW))\n",
    "    \n",
    "        return TR.iloc[-60:].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################################### 筛选成分股 ################################################\n",
    "\n",
    "class FilterStocks(object):\n",
    "    '''\n",
    "    获取某日的成分股股票\n",
    "    1. 过滤st\n",
    "    2. 过滤上市不足N个月\n",
    "    3. 过滤当月交易不超过N日的股票\n",
    "    ---------------\n",
    "    输入参数：\n",
    "        index_symbol:指数代码,A约等于全市场,800是设置的HS300+ZZ500\n",
    "        watch_date:日期\n",
    "        N:上市不足N月\n",
    "        active_day:过滤交易不足N日的股票\n",
    "    '''\n",
    "\n",
    "    def __init__(self, index_symbol: str, watch_date: str, N: int = 3, active_day: int = 15):\n",
    "\n",
    "        self.__index_symbol = index_symbol\n",
    "        self.__watch_date = parse(watch_date).date()\n",
    "        self.__N = N  # 过滤上市不足N月股票\n",
    "        self.__active_day = active_day  # 交易日期\n",
    "\n",
    "    #####################################  获取并过滤成分股 ##############################\n",
    "\n",
    "    # 获取股票池\n",
    "    @property\n",
    "    def Get_Stocks(self) -> list:\n",
    "        '''\n",
    "        bar_datetime:datetime.date\n",
    "        '''\n",
    "\n",
    "        if self.__index_symbol == 'A':\n",
    "\n",
    "            stockList = get_index_stocks('000002.XSHG', date=self.__watch_date) + get_index_stocks(\n",
    "                '399107.XSHE', date=self.__watch_date)\n",
    "\n",
    "        else:\n",
    "            stockList = get_index_stocks(\n",
    "                self.__index_symbol, date=self.__watch_date)\n",
    "\n",
    "        # 过滤ST\n",
    "        st_data = get_extras(\n",
    "            'is_st', stockList, end_date=self.__watch_date, count=1).iloc[0]\n",
    "\n",
    "        stockList = st_data[st_data == False].index.tolist()\n",
    "\n",
    "        # 剔除停牌、新股及退市股票\n",
    "        stockList = self.delect_stop(stockList, self.__watch_date, self.__N)\n",
    "\n",
    "        # 近15日均有交易的股票\n",
    "        active_stock = self.delect_pause(\n",
    "            stockList, self.__watch_date, self.__active_day)\n",
    "\n",
    "        return active_stock\n",
    "\n",
    "    # 去除上市距beginDate不足 3 个月的股票\n",
    "    @staticmethod\n",
    "    def delect_stop(stocks: list, beginDate: datetime.date,\n",
    "                    n: int = 30 * 3) -> list:\n",
    "\n",
    "        return [\n",
    "            code for code in stocks\n",
    "            if get_security_info(code).start_date < (beginDate -\n",
    "                                                     datetime.timedelta(days=n))\n",
    "        ]\n",
    "\n",
    "    # 近15日内有交易\n",
    "    @staticmethod\n",
    "    def delect_pause(stocks: list, beginDate: datetime.date, n: int = 15) -> list:\n",
    "\n",
    "        beginDate = get_trade_days(end_date=beginDate, count=1)[\n",
    "            0].strftime('%Y-%m-%d')\n",
    "\n",
    "        # 获取过去22日的交易数据\n",
    "        df = get_price(\n",
    "            stocks, end_date=beginDate, count=22, fields='paused', panel=False)\n",
    "\n",
    "        # 当日交易\n",
    "        t_trade = df.query('paused==0 and time==@beginDate')[\n",
    "            'code'].values.tolist()\n",
    "\n",
    "        # 当日交易 且 15日都有交易记录\n",
    "        total_num = df[df['code'].isin(t_trade)].groupby('code')[\n",
    "            'paused'].sum()\n",
    "\n",
    "        return total_num[total_num < n].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_factor(func, index_symbol: str, start: str, end: str, freq: str = 'ME') -> pd.DataFrame:\n",
    "    \n",
    "    '''\n",
    "    因子获取\n",
    "    ---------\n",
    "        func:为因子获取函数\n",
    "        index_symbol:成分股代码\n",
    "        freq:日期频率\n",
    "    '''\n",
    "\n",
    "    periods = GetTradePeriod(start, end, freq)\n",
    "\n",
    "    factor_dic = {}\n",
    "    for d in tqdm_notebook(periods):\n",
    "\n",
    "        securities = FilterStocks(\n",
    "            index_symbol, d.strftime('%Y-%m-%d'), N=12).Get_Stocks\n",
    "        factor_dic[d] = func(securities, d)\n",
    "\n",
    "    factor_df = pd.concat(factor_dic)\n",
    "    factor_df.index.names = ['date', 'code']\n",
    "\n",
    "    return factor_df\n",
    "\n",
    "# 获取年末季末时点\n",
    "\n",
    "def GetTradePeriod(start_date: str, end_date: str, freq: str = 'ME') -> list:\n",
    "    '''\n",
    "    start_date/end_date:str YYYY-MM-DD\n",
    "    freq:M月，Q季,Y年 默认ME E代表期末 S代表期初\n",
    "    ================\n",
    "    return  list[datetime.date]\n",
    "    '''\n",
    "    days = pd.Index(pd.to_datetime(get_trade_days(start_date, end_date)))\n",
    "    idx_df = days.to_frame()\n",
    "\n",
    "    if freq[-1] == 'E':\n",
    "        day_range = idx_df.resample(freq[0]).last()\n",
    "    else:\n",
    "        day_range = idx_df.resample(freq[0]).first()\n",
    "\n",
    "    day_range = day_range[0].dt.date\n",
    "\n",
    "    return day_range.dropna().values.tolist()\n",
    "\n",
    "\n",
    "def query_model1_factor(securities: list, watch_date: str) -> pd.DataFrame:\n",
    "    '''获取天风证券 指数增强模型'''\n",
    "    \n",
    "    import warnings\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    \n",
    "    fields = ['natural_log_of_market_cap', 'book_to_price_ratio',\n",
    "              'ROC20', 'ROC60',\n",
    "              'net_profit_growth_rate', 'operating_revenue_growth_rate',\n",
    "              'total_profit_growth_rate', 'roe_ttm',\n",
    "              'roa_ttm', 'VOL20',\n",
    "              'VOL60']\n",
    "\n",
    "    part_a = get_factor_values(\n",
    "        securities, fields, start_date=watch_date, end_date=watch_date)\n",
    "    part_a = dict2frame(part_a)\n",
    "\n",
    "    # 自定义因子\n",
    "    fields = [EPTTM(), SPTTM(), SUE0(),SUR0(), DELTAROE(), DELTAROA(),\n",
    "              ILLIQ(), ATR1M(), ATR3M()]\n",
    "\n",
    "    part_b = calc_factors(securities, fields,\n",
    "                          start_date=watch_date, end_date=watch_date)\n",
    "    part_b = dict2frame(part_b)\n",
    "\n",
    "    # 辅助项\n",
    "    part_c = IndusrtyMktcap(securities, watch_date)\n",
    "\n",
    "    factor_df = pd.concat([part_a, part_b, part_c], axis=1)\n",
    "\n",
    "    return factor_df\n",
    "\n",
    "\n",
    "def dict2frame(dic: dict) -> pd.DataFrame:\n",
    "    \n",
    "    '''将data的dict格式转为df'''\n",
    "\n",
    "    tmp_v = [v.T for v in dic.values()]\n",
    "    name = [k.upper() for k in dic.keys()]\n",
    "\n",
    "    df = pd.concat(tmp_v, axis=1)\n",
    "    df.columns = name\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def IndusrtyMktcap(securities: list, watch_date: str) -> pd.DataFrame:\n",
    "    \n",
    "    '''增加辅助 行业及市值'''\n",
    "\n",
    "    #indusrty_dict = get_industry(securities, watch_date)\n",
    "\n",
    "    #indusrty_ser = pd.Series({k: v.get('sw_l1', {'industry_code': np.nan})[\n",
    "    #                        'industry_code'] for k, v in indusrty_dict.items()})\n",
    "\n",
    "    #indusrty_ser.name = 'INDUSTRY_CODE'\n",
    "    \n",
    "    industry_ser = get_stock_ind(securities,watch_date)\n",
    "    \n",
    "    mkt_cap = get_valuation(securities, end_date=watch_date,\n",
    "                            fields='market_cap', count=1).set_index('code')['market_cap']\n",
    "    \n",
    "    return pd.concat([industry_ser, mkt_cap], axis=1)\n",
    "\n",
    "\n",
    "def get_stock_ind(securities:list,watch_date:str,level:str='sw_l1',method:str='industry_code')->pd.Series:\n",
    "    \n",
    "    '''\n",
    "    获取行业\n",
    "    --------\n",
    "        securities:股票列表\n",
    "        watch_date:查询日期\n",
    "        level:查询股票所属行业级别\n",
    "        method:返回行业名称or代码\n",
    "    '''\n",
    "    \n",
    "    indusrty_dict = get_industry(securities, watch_date)\n",
    "\n",
    "    indusrty_ser = pd.Series({k: v.get('sw_l1', {method: np.nan})[\n",
    "                             method] for k, v in indusrty_dict.items()})\n",
    "    \n",
    "    indusrty_ser.name = method.upper()\n",
    "    \n",
    "    return indusrty_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置时间范围\n",
    "START_DATE = '2010-01-01'\n",
    "END_DATE = '2020-09-30'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c467e257a2f4e77b1f18e1ed2607712",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=129), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 因子获取\n",
    "factors = get_factor(query_model1_factor,'000300.XSHG',START_DATE,END_DATE)\n",
    "# 因子储存\n",
    "factors.to_csv('../../Data/index_enhancement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NATURAL_LOG_OF_MARKET_CAP</th>\n",
       "      <th>ROE_TTM</th>\n",
       "      <th>ROC60</th>\n",
       "      <th>TOTAL_PROFIT_GROWTH_RATE</th>\n",
       "      <th>BOOK_TO_PRICE_RATIO</th>\n",
       "      <th>VOL60</th>\n",
       "      <th>NET_PROFIT_GROWTH_RATE</th>\n",
       "      <th>OPERATING_REVENUE_GROWTH_RATE</th>\n",
       "      <th>ROA_TTM</th>\n",
       "      <th>VOL20</th>\n",
       "      <th>ROC20</th>\n",
       "      <th>EPTTM</th>\n",
       "      <th>SPTTM</th>\n",
       "      <th>SUE0</th>\n",
       "      <th>SUR0</th>\n",
       "      <th>DELTAROE</th>\n",
       "      <th>DELTAROA</th>\n",
       "      <th>ILLIQ</th>\n",
       "      <th>ATR1M</th>\n",
       "      <th>ATR3M</th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>market_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-29</th>\n",
       "      <th>000001.XSHE</th>\n",
       "      <td>24.933731</td>\n",
       "      <td>0.048954</td>\n",
       "      <td>-13.012048</td>\n",
       "      <td>-0.807940</td>\n",
       "      <td>-0.115760</td>\n",
       "      <td>1.184333</td>\n",
       "      <td>-0.771709</td>\n",
       "      <td>0.080103</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>1.6255</td>\n",
       "      <td>-10.974106</td>\n",
       "      <td>0.013867</td>\n",
       "      <td>0.220897</td>\n",
       "      <td>-0.452525</td>\n",
       "      <td>-2.687988</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.946742e-11</td>\n",
       "      <td>37.6780</td>\n",
       "      <td>34.043333</td>\n",
       "      <td>801190</td>\n",
       "      <td>673.8791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.XSHE</th>\n",
       "      <td>25.355032</td>\n",
       "      <td>0.134913</td>\n",
       "      <td>-20.568928</td>\n",
       "      <td>-0.100661</td>\n",
       "      <td>0.349070</td>\n",
       "      <td>1.662500</td>\n",
       "      <td>-0.031681</td>\n",
       "      <td>0.093374</td>\n",
       "      <td>0.043826</td>\n",
       "      <td>1.2635</td>\n",
       "      <td>-13.571429</td>\n",
       "      <td>0.045905</td>\n",
       "      <td>0.467333</td>\n",
       "      <td>-0.506641</td>\n",
       "      <td>-0.344532</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.225025e-11</td>\n",
       "      <td>29.5635</td>\n",
       "      <td>34.869000</td>\n",
       "      <td>801180</td>\n",
       "      <td>1026.9526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.XSHE</th>\n",
       "      <td>23.154818</td>\n",
       "      <td>0.112600</td>\n",
       "      <td>-22.411348</td>\n",
       "      <td>0.240366</td>\n",
       "      <td>-1.068521</td>\n",
       "      <td>2.718333</td>\n",
       "      <td>0.321439</td>\n",
       "      <td>0.121046</td>\n",
       "      <td>0.051588</td>\n",
       "      <td>3.2410</td>\n",
       "      <td>-5.034722</td>\n",
       "      <td>0.018742</td>\n",
       "      <td>0.284552</td>\n",
       "      <td>-0.749767</td>\n",
       "      <td>-3.326327</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.06</td>\n",
       "      <td>9.838215e-11</td>\n",
       "      <td>2.4855</td>\n",
       "      <td>2.332833</td>\n",
       "      <td>801180</td>\n",
       "      <td>113.7653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000012.XSHE</th>\n",
       "      <td>23.828489</td>\n",
       "      <td>0.089591</td>\n",
       "      <td>5.678233</td>\n",
       "      <td>-0.377353</td>\n",
       "      <td>-0.579774</td>\n",
       "      <td>2.808667</td>\n",
       "      <td>-0.350274</td>\n",
       "      <td>0.031055</td>\n",
       "      <td>0.047857</td>\n",
       "      <td>2.0010</td>\n",
       "      <td>-6.815021</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>0.203749</td>\n",
       "      <td>1.823341</td>\n",
       "      <td>5.050380</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.451669e-10</td>\n",
       "      <td>9.0155</td>\n",
       "      <td>8.176333</td>\n",
       "      <td>801060</td>\n",
       "      <td>223.1420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000021.XSHE</th>\n",
       "      <td>23.162907</td>\n",
       "      <td>0.076904</td>\n",
       "      <td>-0.497512</td>\n",
       "      <td>-0.402697</td>\n",
       "      <td>0.094594</td>\n",
       "      <td>1.484333</td>\n",
       "      <td>-0.299258</td>\n",
       "      <td>-0.217991</td>\n",
       "      <td>0.052197</td>\n",
       "      <td>1.6980</td>\n",
       "      <td>1.137800</td>\n",
       "      <td>0.023767</td>\n",
       "      <td>0.936417</td>\n",
       "      <td>-0.444106</td>\n",
       "      <td>-0.024002</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>1.267343e-10</td>\n",
       "      <td>6.1770</td>\n",
       "      <td>5.707167</td>\n",
       "      <td>801100</td>\n",
       "      <td>114.6892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        NATURAL_LOG_OF_MARKET_CAP     ...      market_cap\n",
       "date       code                                       ...                \n",
       "2010-01-29 000001.XSHE                  24.933731     ...        673.8791\n",
       "           000002.XSHE                  25.355032     ...       1026.9526\n",
       "           000009.XSHE                  23.154818     ...        113.7653\n",
       "           000012.XSHE                  23.828489     ...        223.1420\n",
       "           000021.XSHE                  23.162907     ...        114.6892\n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因子读取\n",
    "factors = pd.read_csv('../../Data/index_enhancement.csv',index_col=[0,1],parse_dates=[0],dtype={'INDUSTRY_CODE':str})\n",
    "\n",
    "# 查看数据结构\n",
    "factors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 37366 entries, (2010-01-29 00:00:00, 000001.XSHE) to (2020-09-30 00:00:00, 603993.XSHG)\n",
      "Data columns (total 22 columns):\n",
      "NATURAL_LOG_OF_MARKET_CAP        37366 non-null float64\n",
      "ROE_TTM                          37340 non-null float64\n",
      "ROC60                            37366 non-null float64\n",
      "TOTAL_PROFIT_GROWTH_RATE         35612 non-null float64\n",
      "BOOK_TO_PRICE_RATIO              37366 non-null float64\n",
      "VOL60                            37366 non-null float64\n",
      "NET_PROFIT_GROWTH_RATE           35612 non-null float64\n",
      "OPERATING_REVENUE_GROWTH_RATE    35574 non-null float64\n",
      "ROA_TTM                          37336 non-null float64\n",
      "VOL20                            37366 non-null float64\n",
      "ROC20                            37366 non-null float64\n",
      "EPTTM                            37366 non-null float64\n",
      "SPTTM                            37366 non-null float64\n",
      "SUE0                             37363 non-null float64\n",
      "SUR0                             37348 non-null float64\n",
      "DELTAROE                         36535 non-null float64\n",
      "DELTAROA                         36531 non-null float64\n",
      "ILLIQ                            37366 non-null float64\n",
      "ATR1M                            37366 non-null float64\n",
      "ATR3M                            37366 non-null float64\n",
      "INDUSTRY_CODE                    37365 non-null object\n",
      "market_cap                       37366 non-null float64\n",
      "dtypes: float64(21), object(1)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、数据预处理\n",
    "\n",
    "## 3.1缺失值处理\n",
    "\n",
    "对因子值有缺失的股票视情况补其因子值为行业中位数(或0值填充)，如果行业缺失则予以删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step1:构建缺失值处理函数\n",
    "def factors_null_process(data: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    # 删除行业缺失值\n",
    "    data = data[data['INDUSTRY_CODE'].notnull()]\n",
    "\n",
    "    # 变化索引，以行业为第一索引，股票代码为第二索引\n",
    "    data_ = data.reset_index().set_index(\n",
    "        ['INDUSTRY_CODE', 'code']).sort_index()\n",
    "    # 用行业中位数填充\n",
    "    data_ = data_.groupby(level=0).apply(\n",
    "        lambda factor: factor.fillna(factor.median()))\n",
    "    \n",
    "    # 有些行业可能只有一两个个股却都为nan此时使用0值填充\n",
    "    data_ = data_.fillna(0)\n",
    "    # 将索引换回\n",
    "    data_ = data_.reset_index().set_index('code').sort_index()\n",
    "    return data_.drop('date', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2去极值\n",
    "\n",
    "我们采用MAD（Median Absolute Deviation 绝对中位数法）去极值，对于极值部分将其均匀插值到 3-3.5 倍绝对中位数范围内。具体操作如下，首先计算当期所有股 票在因子f上的中位数$m_{f}$，然后计算绝对中位数\n",
    "$$𝑀𝐴𝐷 = 𝑚𝑒𝑑𝑖𝑎𝑛(|𝑓 − 𝑚_𝑓|)$$\n",
    "\n",
    "采用与$3\\sigma$法等价的方法，保留 $[𝑚_𝑓 − 3 ∙ 1.483 ∙ 𝑀𝐴𝐷，𝑚_𝑓 + 3 ∙ 1.483 ∙ 𝑀𝐴𝐷]$ 之间股票 的因子值不变，取值大于 $𝑚_𝑓$ + 3 ∙ 1.483 ∙ 𝑀𝐴𝐷 的所有股票的因子取值按排序均匀压缩到 $[𝑚_𝑓 + 3 ∙ 1.483 ∙ 𝑀𝐴𝐷, 𝑚_𝑓 + 3.5 ∙ 1.483 ∙ 𝑀𝐴𝐷]$ 之间，取值低于$m_f$ − 3 ∙ 1.483 ∙ 𝑀𝐴𝐷 的所 有股票的因子取值按排序均匀压缩到$[𝑚_𝑓 − 3.5 ∙ 1.483 ∙ 𝑀𝐴𝐷, 𝑚_𝑓 − 3 ∙ 1.483 ∙ 𝑀𝐴𝐷]$之间，这样去除了极值同时也在极值的股票之间保序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step2:构建绝对中位数处理法函数\n",
    "def extreme_process_MAD(data: pd.DataFrame, num: int = 3) -> pd.DataFrame:\n",
    "    ''' data为输入的数据集，如果数值超过num个判断标准则使其等于num个标准'''\n",
    "\n",
    "    # 为不破坏原始数据，先对其进行拷贝\n",
    "    data_ = data.copy()\n",
    "\n",
    "    # 获取数据集中需测试的因子名\n",
    "    feature_names = [i for i in data_.columns.tolist() if i not in [\n",
    "        'INDUSTRY_CODE','market_cap']]\n",
    "\n",
    "    # 获取中位数\n",
    "    median = data_[feature_names].median(axis=0)\n",
    "    # 按列索引匹配，并在行中广播\n",
    "    MAD = abs(data_[feature_names].sub(median, axis=1)\n",
    "              ).median(axis=0)\n",
    "    # 利用clip()函数，将因子取值限定在上下限范围内，即用上下限来代替异常值\n",
    "    data_.loc[:, feature_names] = data_.loc[:, feature_names].clip(\n",
    "        lower=median-num * 1.4826 * MAD, upper=median + num * 1.4826 * MAD, axis=1)\n",
    "    return data_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3标准化\n",
    "\n",
    "为了使得构造复合因子时各因子间量纲统一，我们对每个因子进行标准化处 理，我们采用 Z-Score 方法来对因子取值标准化，使得因子的均值为 0，标准差为 1，即\n",
    "\n",
    "$$f'=\\frac{f-mean(f)}{std(f)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##step3:构建标准化处理函数\n",
    "def data_scale_Z_Score(data: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    # 为不破坏原始数据，先对其进行拷贝\n",
    "    data_ = data.copy()\n",
    "    # 获取数据集中需测试的因子名\n",
    "    feature_names = [i for i in data_.columns.tolist() if i not in [\n",
    "        'INDUSTRY_CODE','market_cap']]\n",
    "    data_.loc[:, feature_names] = (\n",
    "        data_.loc[:, feature_names] - data_.loc[:, feature_names].mean()) / data_.loc[:, feature_names].std()\n",
    "    return data_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4市值和行业中性化\n",
    "\n",
    "由于因子可能受到市值以及行业的影响较大，因此需要对市值和 行业进行中性化处理，即对下式做回归取残差：\n",
    "\n",
    "$$f_i=\\beta^{MV}MV_i+\\Sigma\\beta^{ind}_jX_{ij}+\\epsilon$$\n",
    "\n",
    "其中$𝑀𝑉_𝑖$为股票𝑖的对数总市值，也进行了去极值、标准化的处理，$𝑋_{𝑖𝑗}$为股票 𝑖 对于行业 𝑗 的0-1哑变量，对回归后得到的残差$\\epsilon$继续做去极值、标准化处理得到中性化后的因子取值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step4:因子中性化处理函数\n",
    "def neutralization(data: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    '''按市值、行业进行中性化处理 ps:处理后无行业市值信息'''\n",
    "\n",
    "    factor_name = [i for i in data.columns.tolist() if i not in [\n",
    "        'INDUSTRY_CODE', 'market_cap']]\n",
    "    \n",
    "    # 回归取残差\n",
    "    def _calc_resid(x: pd.DataFrame, y: pd.Series) -> float:\n",
    "\n",
    "        result = sm.OLS(y, x).fit()\n",
    "\n",
    "        return result.resid\n",
    "    \n",
    "    X = pd.get_dummies(data['INDUSTRY_CODE'])\n",
    "    # 总市值单位为亿元\n",
    "    X['market_cap'] = np.log(data['market_cap'] * 100000000)\n",
    "    \n",
    "    df = pd.concat([_calc_resid(X.fillna(0), data[i])\n",
    "                    for i in factor_name], axis=1)\n",
    "    \n",
    "    df.columns = factor_name\n",
    "    \n",
    "    df['INDUSTRY_CODE'] = data['INDUSTRY_CODE']\n",
    "    df['market_cap'] = data['market_cap']\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## 其实可以直接pipe处理但是这里为了后续灵活性没有选择pipe化\n",
    "\n",
    "# 去极值\n",
    "factors1 = factors.groupby(level='date').apply(extreme_process_MAD)\n",
    "# 缺失值处理\n",
    "factors2 = factors1.groupby(level='date').apply(factors_null_process)\n",
    "# 中性化\n",
    "factors3 = factors2.groupby(level='date').apply(neutralization)\n",
    "# 标准化\n",
    "factors4 = factors3.groupby(level='date').apply(data_scale_Z_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 37366 entries, (2010-01-29 00:00:00, 000001.XSHE) to (2020-09-30 00:00:00, 603993.XSHG)\n",
      "Data columns (total 22 columns):\n",
      "NATURAL_LOG_OF_MARKET_CAP        37366 non-null float64\n",
      "ROE_TTM                          37340 non-null float64\n",
      "ROC60                            37366 non-null float64\n",
      "TOTAL_PROFIT_GROWTH_RATE         35612 non-null float64\n",
      "BOOK_TO_PRICE_RATIO              37366 non-null float64\n",
      "VOL60                            37366 non-null float64\n",
      "NET_PROFIT_GROWTH_RATE           35612 non-null float64\n",
      "OPERATING_REVENUE_GROWTH_RATE    35574 non-null float64\n",
      "ROA_TTM                          37336 non-null float64\n",
      "VOL20                            37366 non-null float64\n",
      "ROC20                            37366 non-null float64\n",
      "EPTTM                            37366 non-null float64\n",
      "SPTTM                            37366 non-null float64\n",
      "SUE0                             37363 non-null float64\n",
      "SUR0                             37348 non-null float64\n",
      "DELTAROE                         36535 non-null float64\n",
      "DELTAROA                         36531 non-null float64\n",
      "ILLIQ                            37366 non-null float64\n",
      "ATR1M                            37366 non-null float64\n",
      "ATR3M                            37366 non-null float64\n",
      "INDUSTRY_CODE                    37365 non-null object\n",
      "market_cap                       37366 non-null float64\n",
      "dtypes: float64(21), object(1)\n",
      "memory usage: 7.7+ MB\n",
      "None\n",
      "去缺失值:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 37365 entries, (2010-01-29 00:00:00, 000001.XSHE) to (2020-09-30 00:00:00, 603993.XSHG)\n",
      "Data columns (total 22 columns):\n",
      "INDUSTRY_CODE                    37365 non-null object\n",
      "NATURAL_LOG_OF_MARKET_CAP        37365 non-null float64\n",
      "ROE_TTM                          37365 non-null float64\n",
      "ROC60                            37365 non-null float64\n",
      "TOTAL_PROFIT_GROWTH_RATE         37365 non-null float64\n",
      "BOOK_TO_PRICE_RATIO              37365 non-null float64\n",
      "VOL60                            37365 non-null float64\n",
      "NET_PROFIT_GROWTH_RATE           37365 non-null float64\n",
      "OPERATING_REVENUE_GROWTH_RATE    37365 non-null float64\n",
      "ROA_TTM                          37365 non-null float64\n",
      "VOL20                            37365 non-null float64\n",
      "ROC20                            37365 non-null float64\n",
      "EPTTM                            37365 non-null float64\n",
      "SPTTM                            37365 non-null float64\n",
      "SUE0                             37365 non-null float64\n",
      "SUR0                             37365 non-null float64\n",
      "DELTAROE                         37365 non-null float64\n",
      "DELTAROA                         37365 non-null float64\n",
      "ILLIQ                            37365 non-null float64\n",
      "ATR1M                            37365 non-null float64\n",
      "ATR3M                            37365 non-null float64\n",
      "market_cap                       37365 non-null float64\n",
      "dtypes: float64(21), object(1)\n",
      "memory usage: 7.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(factors.info())\n",
    "print('去缺失值:')\n",
    "print(factors2.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 储存处理后的数据\n",
    "factors4.to_csv('factors4.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、因子多重共线性的处理\n",
    "\n",
    "在构建多因子选股模型时，我们通常根据多个因子的线性加权来为个股进行综合打分， 即以下形式 \n",
    "$$𝐹 = 𝑣^1 ∙ 𝑓^1 + 𝑣^2 ⋅ 𝑓^2 +⋯+ 𝑣^𝐾 ⋅ 𝑓^𝐾$$\n",
    "其中 𝐾 表示因子的数量， 𝑓𝐾 表示股票在第 𝐾 个因子上的取值，𝑣𝐾 表示因子的权重。这种打分方式有一个很重要的隐含假设是因子之间的相关性较低。这里我们使用**对称正交**来处理共线性的问题。\n",
    "\n",
    "对称正交有几个重要的性质 [Klein 2013]：\n",
    "1. 相对于施密特正交法，对称正交不需要提供正交次序，对每个因子平等看待； \n",
    "2. 在所有正交过渡矩阵中，对称正交后的矩阵和原始矩阵的相似性最大，即正交前后矩 阵的距离最小，对原始因子矩阵的修改最小；\n",
    "3. 对称正交的计算只需要截面因子数据，计算效率非常高。\n",
    "从这些性质出发，对称正交后的因子和原始因子有较好的对应关系，因子的经济意义保持 能力较好，并且在 Frobenius 范数下保持了最高的相似性。为了方便直观理解，我们以两个因子𝑓1, 𝑓2 分别进行施密特正交和对称正交的示意图:\n",
    "![avatar]()\n",
    "从图中可以看出，施密特正交中排序越高的因子旋转角度越大，对因子并没有平等对待；而对称正交中因子都各自旋转了同样很小的角度来得到正交基，对因子平等对待，且正交前后因子的对应关系保持的很好。\n",
    "\n",
    "现在知道对称正交的优势了吧 ,下面来看看该方法具体的过渡矩阵时如何计算的：\n",
    "我们定义一个从$F_{N*K}$旋转到$\\widetilde{F_{N*K}} $的`过渡矩阵`$S_{K*K}$，公式如下所示：  \n",
    "\n",
    "$$S_{K*K}=U_{K*K}D_{K*K}^{-1/2}U_{K*K}^{'}$$\n",
    "\n",
    "其中，记矩阵$M=F_{N*K}^{'}F_{N*K} $,$D_{K*K}$为M的特征根构成的对角阵，$U_{K*K}$为每一列由M的特征向量构成的矩阵。$S_{K*K}$是一个对角矩阵，所以我们才称之为对称正交,$F_{N*K}$的旋转公式如下：\n",
    "\n",
    "$$\\widetilde{F_{N*K}} =F_{N*K}S_{K*K}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## step5:构建对称正交变换函数\n",
    "def lowdin_orthogonal(data:pd.DataFrame)->pd.DataFrame:\n",
    "\n",
    "    data_ = data.copy() # 创建副本不影响原数据\n",
    "    col = [col for col in data_.columns if col not in ['INDUSTRY_CODE','market_cap']]\n",
    "    \n",
    "    F = np.mat(data_[col])  # 除去行业指标,将数据框转化为矩阵\n",
    "    M = F.T @ F # 等价于 (F.shape[0] - 1) * np.cov(F.T)\n",
    "    a,U = np.linalg.eig(M)  # a为特征值，U为特征向量\n",
    "    D_inv = np.linalg.inv(np.diag(a))\n",
    "    S = U @ np.sqrt(D_inv) @ U.T\n",
    "    data_[col] = data_[col].dot(S)\n",
    "    \n",
    "    return data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 37365 entries, (2010-01-29 00:00:00, 000001.XSHE) to (2020-09-30 00:00:00, 603993.XSHG)\n",
      "Data columns (total 22 columns):\n",
      "NATURAL_LOG_OF_MARKET_CAP        37365 non-null float64\n",
      "ROE_TTM                          37365 non-null float64\n",
      "ROC60                            37365 non-null float64\n",
      "TOTAL_PROFIT_GROWTH_RATE         37365 non-null float64\n",
      "BOOK_TO_PRICE_RATIO              37365 non-null float64\n",
      "VOL60                            37365 non-null float64\n",
      "NET_PROFIT_GROWTH_RATE           37365 non-null float64\n",
      "OPERATING_REVENUE_GROWTH_RATE    37365 non-null float64\n",
      "ROA_TTM                          37365 non-null float64\n",
      "VOL20                            37365 non-null float64\n",
      "ROC20                            37365 non-null float64\n",
      "EPTTM                            37365 non-null float64\n",
      "SPTTM                            37365 non-null float64\n",
      "SUE0                             37365 non-null float64\n",
      "SUR0                             37365 non-null float64\n",
      "DELTAROE                         37365 non-null float64\n",
      "DELTAROA                         37365 non-null float64\n",
      "ILLIQ                            37365 non-null float64\n",
      "ATR1M                            37365 non-null float64\n",
      "ATR3M                            37365 non-null float64\n",
      "INDUSTRY_CODE                    37365 non-null object\n",
      "market_cap                       37365 non-null float64\n",
      "dtypes: float64(21), object(1)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# 数据读取\n",
    "factors4 = pd.read_csv('factors4.csv',index_col=[0,1],parse_dates=[0],dtype={'INDUSTRY_CODE':str})\n",
    "\n",
    "#对称正交化\n",
    "factors5 = factors4.groupby(level='date').apply(lowdin_orthogonal)  \n",
    "factors5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建计算横截面因子载荷相关系数均值函数\n",
    "def get_relations(datas: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    relations = 0\n",
    "    for trade,d in datas.groupby(level='date'):\n",
    "        \n",
    "        relations += d.corr()\n",
    "\n",
    "    relations_mean = relations / len(datas.index.levels[0])\n",
    "\n",
    "    return relations_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "未进行正交处理,各因子的相关性如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f561855fcf8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1872x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制因子正交前的相关性的热力图\n",
    "fig = plt.figure(figsize=(26, 18))\n",
    "# 计算对称正交之前的相关系数矩阵\n",
    "relations = get_relations(factors4.iloc[:,:-2])  \n",
    "sns.heatmap(relations, annot=True, linewidths=0.05,\n",
    "            linecolor='white', annot_kws={'size': 8, 'weight': 'bold'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正交处理后各因子相关性明显下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f2aa4e128>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WBg4cRbMWnRgydALdu3UgPCyU1q1fv6f84sUKU6Dg/wCIiblms/6bVnsaxlO8+uorVHyxDlevRtGpo1+G2vPc+d9JSEigc5d3OXjo2D1v/rJKWnH6+vqQN18eypStwY6IPXQLap+h623Bgv+jePGiwJ3rke3G6cCBvZg4aTrPla1Bxw5vUKRIwWTPc+Tr7Z0lGYoWK8z8uR9TrtxzjL/rWnHu/AV++/UcjRq3pXCRgtStW8Nh758PMzc3NwK7tGX0mGD6DRjOuHGDHP7+GbF9NVWrvsSNG7FERSceS84070vuOpQnT2769e2e6J5qwcLWbbvwqdOSwK59cXFxITwsNNnlZP788+8U58z2rEOn3rT268rkKXNTvRedP/87h48cp76vH/Xq1qRihedtUNv0c8bxmZn3FXuZJ6QV54sVy1GkcEGeK1OdZ54pRcOGtR0yTrj16Ynjx7+jcpWGjBzxHnny5LYe69unG9NnLAAcez70sEhwcXH4r6zmYrFYsvyHivwXhmEUA45xK8l+hwnsBkYAPwIuwHngXdM0z9312p+BMqZpJsoaG4axDehhmubx29sdbh8qfftn5ebWP4DOA/xsmuaCVKpoecQj+TfU6dGmTXO6B3WgUqUKRETsoWLFcjxXtjq9enSmga8PBZ58gsOHj9GgYRtCFk+jcOGCRF2Nxq9NEJcu/QFA/vz5WLZ0FuXKPcfFS3+wZMkKRo669RHWO+vhDx/x0X+uI0B87FnuJ06A/fs2Mn7CNHbs2M2SxdOp7+vPvLkfUeXll8ie3Ytd3+yjc5c+rFwxn2zZsnHx0mX8/Ltx/fqtp13LlHmG4I9HUKlSBU59f5rp0xeQM2cO2rZtiYsLjB4zmeXL0/4fApkdZ7eg9rRr2xLv3N588OEYrsVco1GjuvTs9f49P6d+vVqMGzeIGzdimTdvCbNmL7Ke0+fdIPz8mlH62VLs3n2AjgFvM3RIP54uVQJPL0/823Tj1KkfbRLnK1X/j2nTxhIXF88XX6ymXt2a1Pf1Z+GCyRQvXoRcuXIyaPA4tmzZ6dDtmdL4rFixHAP6vYV3bm+mTp3HnLkhLF0yI93j8/sffmJA/x4kJFhYuHAZU6bOs2mcAIsWTkk0FufODaFRo7oMGz7xnvqvWxfOnDkfERcXx6ZN2xg8ZHyKv7P+A0aw/8AR4Na6wT51WvDLL7/ZLM4Fn07GMJ7C3d2d4MlzcMGFIkUKMHPWItauXoSrmxv79h2mZ6/3ade2FX36BHHjRiyjRn/C6tUbrOVMGDc40TW6Q6feLA2ZicVi4cqVq7RqHWhdNswWcabUnuPGT2XZ0lkkJCTgld0L/zbdyJkje4bas0mT+tSqWZVc3jnp1q0/u77Zb3dxjhz1MaHL5hAfH0+OnDkICurHE088lu7rbYdOvZk9cyI5c+YgISGB1v5BnD9/wSZxtmjxGu8P7EV0VAzR0TF4eXlSu24rp7x/3hEeFsrwER9RtGhhihQpwMRJM/l86UzyP5oPi8VCsxadqFjheZvdP+Mun077xAdkzYZwPl+xlsiTJhXLlaFvj86ULW1k+s91z18iU+6fb/fuSrVqlfDy8mTGjAXMnrPY4e+fE8YNpkbNKri6uvLuu4Px8vJ0unlfctehOvXeYPasiYnuqTt27ObzpbOwJCRw4uQpugT+u750bZ9qDHyvFzVqVGH//sOMGhNM4UIFEs2ZV61a/5/r+CDiTCppP96ydSfNmjXkxo1Y3n13MN/sPmA9N+n96K0eAwn5bDq5vHNy6eJlWr4RaL0m36/Mmt864/i840HfV+xpnpBanGu/CmPunI+4cf2Gw8fp79+Mfn27k5BgITLyJEUKF6BDp978889V/rz8HQUKlePixcvkzu1t8/kQt/JTkoLlT7Zx+IR0i/MhWdrGSuKLw7idxF9umuaLSfZ34FaCvm8qr/2ZjCXxTwCDAG+gLRBAJifxHcWDnhTbK8XpXBSnc1GczkVxOpeHKc6sTOLbyv0m8R3Fw9RvFafzUJzORXE6FyXx06YkfsbpMyIiyTBNcz9QDHjENM1M/6e2IiIiIiIiIiIiIsl5xNYVEMkg4/bT83dbl0k/ayNgP/8NVkRERERERERExME5/GP4NqAkvjgM0zR/BnL9x9cWS2F/zSTbC+76vt9d3w/9Lz9XRERERERERERE5H5oOR0RERERERERERERETulJ/FFREREREREREREJEsk2LoCDkhP4ouIiIiIiIiIiIiI2Ckl8UVERERERERERERE7JSS+CIiIiIiIiIiIiIidkpr4ouIiIiIiIiIiIhIlkhwsXUNHI+exBcRERERERERERERsVNK4ouIiIiIiIiIiIiI2Ckl8UVERERERERERERE7JTWxBcRERERERERERGRLJGAFsXPKD2JLyIiIiIiIiIiIiJip5TEFxERERERERERERGxU0rii4iIiIiIiIiIiIjYKa2JLyIiIiIiIiIiIiJZwmLrCjggPYkvIiIiIiIiIiIiImKn9CS+iIiIiIiIiIiIiMgDZBjGx0Blbn34oLdpmvuTOWcM8LJpmjVTK0tP4ouIiIiIiIiIiIiIPCCGYdQASpmm+TIQAExO5pzSQPX0lKckvoiIiIiIiIiIiIhkiQQXx/9KBx9gFYBpmt8CeQ3D8E5yziTgg/QUpuV0RB6g+Niztq5CllCczkVxOhfF6VwUp3NRnM7FPX8JW1chSzws7ak4nYvidC6K07k8LHGKAP8DDt61fen2visAhmF0ALYDP6enMCXxRR6gRzwK2roKmS4+9qzidCKK07koTueiOJ2L4nQuD1OccZdP27oamc49f4mHpj0Vp/NQnM5FcToX/aFCUmB9ft8wjHxAR6A2kK5BoeV0REREREREREREREQenHPcevL+jgLA+dvfvwo8BkQAK4EKt/8Jbor0JL6IiIiIiIiIiIiIZIkEW1cga2wChgGzDMOoAJwzTfMqgGmay4HlAIZhFAMWmKb5TmqF6Ul8EREREREREREREZEHxDTNb4CDhmF8A0wG3jIMo4NhGE3/S3l6El9ERERERERERERE5AEyTfO9JLuOJnPOz0DNtMpSEl9EREREREREREREsoTF1hVwQFpOR0RERERERERERETETimJLyIiIiIiIiIiIiJip5TEFxERERERERERERGxU1oTX0RERERERERERESyRIKLrWvgePQkvoiIiIiIiIiIiIiInVISX0RERERERERERETETimJLyIiIiIiIiIiIiJip7QmvoiIiIiIiIiIiIhkiQRbV8AB6Ul8ERERERERERERERE7pSS+iIiIiIiIiIiIiIidUhJfRERERERERERERMROaU18EREREREREREREckSWhM/4/QkvoiIiIiIiIiIiIiInVISX0RERERERERERETETimJLyIiIiIiIiIiIiJip5TEF4dhGMZbhmHsMQxju2EY+wzDqG0YxlDDML43DGPb7f3rDMMoYBhGndv7thmGEXfX9zXu+v66YRgRt79vfLus75L8zDKGYVgMw6iZ2fHNnjWRvXvWs3PHGp59tpR1f/bsXkwOHkX01dPUq5t2Ncq/UIZdEWv57cxh676PJg1j/76NfLNzLfny5c2M6qfbpIm36nIschvVq1W27s+RIzvrv17C7m++ZlfEWh5/PH+q5ZQoUZSv1nxGfOxZsmXLBkB87FnCw0IJDwulSZP6mRpHekwYN5gD+zdx+NDmRLFmz+7F+q+XELF9NXt2r6NYscKplpO0TYsWLUTUlR+tsVauVDFT40hLrlw5ORa5jXlzP7bua9WqMad/2EfI4ulpvj65Ph4eFsqB/ZsIDwtlyOA+mVX1DEkpJmfru8m1Z0p9OTnJtafPq9U4sH8Tx49tt3l8d6R0LapcqaK1Lc7+eoSuge1SLceex2dyfQ2gd68uHD60maNHttCixWupluHq6srwYf356w+TwC5tAXj++dLs3bOeY5HbCOraPlNjSI+aNapw6GAYhw6GMXbMB9b9Gel3ycU5b+7HHD+2nfCwUKZPG5epMfwX9zsu7fE6C1Db51a7HT2yJVF7Dh3Sl4MHwtj9zddUe6VSqmU4Qrwpjc++fbpx8EAYJ47v+E/j0977bVrO/HaOoD6DKFO1ATduxNq6Oul25zq0d896ZkxP/Htv6FubmKifqFH95VTLeOyxR1m0cArxsWcxjKcA8PNrysEDYRyL3Gbz+V5y7ne+9+WK+YSHhXLwQBjf7FybybVNv4d1fI4fO4j9+zZy+NBmfBv4pHquI/XXIYP7EB4WytbwFVyP+YUcObJbjznb+Pzh1B52RawlPCz0njlsRnICjvh+JaW5fXIcYZ7wMLC4OP5XVlMSXxyCYRjFgC5ANdM0awBtgEG3Dwebplnz9v5lwHDTNMNu76sJ/HPne9M0t9+1/3egwe3tNbfLcjcMo/xdP7o1cDqz48uXLy+dOvrR6LU3WbV6Pe++E2Q95u2di0WLvuD8+YvWffXr1WLbli/ZFbH2ngRKzpw5GDdhqnW7QvmytPFvTpWqjRgydAKurja40tzm7u7O8ePfUqVqIyIi9tKgwavWY/ny5WHxkhW8XKUhubxzUrlyRSqUL0t4WCjbt65k5Ij3EpWVN09uRo0OvudnREdfw8XFhe+++z7T40mNm5sbp3/6hRo1X2fnzn00adLAeuzpp0tQocLzVK/5OlFXo3nllUoZalOAuLh44uLiiYm5xk8/n8mSmFIyOXgU69eFJ9oXdTWazxaHWrdz5/ZmzapFbNqwjFUrF+Dh4WE9llwfB4iOisbLy5MjR09kbgDplDSmO5yt7yZtz+T6ckbbc+LEIYweE0zzFp0488tvWRpPclK7Fu3ZexCfOi0J6j6AP/78i8Uhyx12fKbU1/755woNfP357LNQ/Fq/jru7OyGLp7NpwzLCNn5B/vz5rOd6eHiwY8duDh2KtO4bPqw/y5atooGvPydOfHdP+VnNw8OdLl360LVrPzoHtLHuT9rvMhonQHR0DJ6enhw5cjzL4kmPBzEuwf6uswAWC7Tv0JOuXfvR9s2W1v29e3UhKKgfkz6ayeBBfRw+3pTG5/nfL1KnbisWLfqCZk19narfpseVq1F07eBn62pkWOPG9di6ZRctW3WmXduWuLm5AVCoUAE6dmzN3r2HgFt/6N20YRmbN4Uyf94nicrIly8PI0b+m5BydXVlSvAoAgP70L59T/76+++sCyid7ne+16x5J3zqtOTihUuMHjs5S+uemod1fAYE+BPQ+R2+/PJrmjb1dZr+Omz4JHzqtGRT2HamTp1PdHQM4Lzj8+rVKLJly8axYyet+5LLCTjT+5Xk5vaOPk8QSY6S+OIocgOegAeAaZrf307aJ7UXKJXM/vRaB/jftV0f2HMf5aXLn3/+xSefzGblik+p7VOdAk8+YT32++8XOXDwaKLzhwzpS+483sTHx9OxY+tExyJ27uWfv69Yt0uWKk5MzDVCFk/j/YG9yZ3bO3ODSUVcXByfLvicWTMn4NvAh5AlX1qP/frrOZYuXclHk4YRE3ONzZt30L9/DwoUeJL4+HhatWyMl5en9fyDhyI5d/73ROU3btKOFq06s2fvQT784J0siys5N2/eZMbMhTRuXI/Gr9Vl3vwQ67HIyG85edLkWOQ2ihYtxIYNWzLUphcuXOaN1oHU9/XjytUoevYIyLK4kuoa2I7IyJMcP2Em2r9ufTg3b960bvv7NeWll17Azc2VkiWLU+XlF63HkuvjH3w4ljf8ujJs+CSmBI/K3CDSKWlMdzhT302uPZPryxltz1Ili9O8WUMWLphC+fJlsyyelKR2Lbpj2pQxjBz1MdHRMQ47PpPrawALFi7judIGvXp2ZsqUedSpXR3fBj64ubny2OOPUq9eLeu5169fZ3N4RKLXlyxZnGrVKrNs6Syqp/HkWlbYFLadP/78i9mzJzJu/L9/UEna7zIaZ/DkOfj5B9Gp89sEfzKC7Nm9siymtDyIcWmP11mA8C0RGEZJvlg2m+DJc6z7R476mMmTR9HGvxlPFnjC4eNNaXyGhKzg3Xe60rNHAHPnhjhVv02PMs8+zeP5H7V1NTJs8eLl+Pk1JTxsOfM//ZybN2/i5ubGzOnj6NN3KAkJCQB0D+pAqVIlcHGBKi+/SMmSxa1lmOaPfP/9v88OPfbYo+TJk5ugoPZMnz6OUiVLZHlcabnf+R5AmzbNSUhI4KuvwrKkzunxsI7PGTMXsnrlQrp368jixcudqr8+9VQxAru0ZdiISQBOOz67BPbhDb+uLPoslHFjB1n3J5cTcKb3K8nN7R19niCSHCXxxSGYpnkU2Af8ZBjGAsMwWhmG8Ugypza6fd5/tR5oZBiGi2EYLwLfAXH3UV6ahg/rT3hYKEciT1CvQWsOHDzKj6d/Sf1FFguffDKb15q0o2fP92nWrCHhYaG83TvwnlPPn7tAVHQ0rd4IJCoqmpZpfPQzswwf1p8D+zfROaANnQLeZtbsRQwd0td63M3NjdUrF+DtnYuatZoRE3MNi8XC58tWUt/Xny6BfaxPC9z98fo78ufPx3PPGcTGxhIVFYOrq+0ub3fadOB7vejVI4BXqjfm5MlT1uO+vj7kzZeHMmVrsCNiD92C2meoTQsW/B/FixcFICoq2qafrmj9RhNea1SH/v3fol7dmtT2qZbseRYLfPvtKXzqtCQg4B0OHDxKeFgo679ekuz5Vau+BNyJz7a3qjvtWatm1WSPO1PfTak9k/bljLbnuXMXCA6ew8RJM+jbt3tWhnSPtK5FcOtppdKln2b58q9u7XDQ8ZmSN99sQXDwSBo09Gfb9m+wWCxcvvwnPnVa0qFjLzZu3MrqlQsJDwslb94897z+/LkLLFm6krffGcT7A3vZIILEnn++NJs3hTJ8xEdMmPjvkg5J+11G43yxYjmyZfMgKioGFxf7aceU7jGOfJ29W+PG9Th+/DsqV2nIyBHvkSdPbgDMUz/SrHknvv56M6d//MVp4r2bm5sbgV3aMnpMMP0GDGfcuEFO02+d3cCBvZg4aTrPla1Bxw5vUKRIQSqUL0vhIgWZP/djypV7jvHjB2PBwtZtu/Cp05LArn1xcXEhPCyUT+ff+8Trn3/+zbVr1/jgwzEsX76Wt7p3tEFkyUtpbpTRcQm3lqiZPmNBJtf4/jnz+Bw+rD8HD4Tx3oCevPh/dRnw3kgGffiu0/RXgJ49AggNXcPVq1EATjc+hw/rT8T21VSt+hI3bsQSFZ34XpdcTsCZ3q/kzu19z9zeGecJzibBCb6ymovFYrHBjxX5bwzDeBaoB7wJXAV2cOvJ+bOAC3AK6GOa5pW7XnPZNM17Fqk2DONnoIxpmlG3t4cC24DuwBSgCbATeB1YYJrmtjSqZ3nEo+B/jm3smA+oU6cm12Ku0aZtd2pUr0KRIgVYtXoDwR+PoFKlCpz6/jTTpy/gt9/OMW7cIG7ciGXevCXMmr3IWk6fd4Pw82tG6WdLsXv3AToGvM3QIf14ulQJPL088W/TjVOnfvzP9YyPPct/jdPNzY3PFk2lePEi5MqVk0GDxxETHUOjRnXZuWsvny2cys6de7FYLMyZF8Ip80fmzPmIuLg4Nm3axuAh461ltWnTnO5BHahUqQIREXsY8N5IevYMoORTxXBxdaVTwNuJEudZGSfAk08+wS8/HSDy2Lf88/c/7D9whK1bd9GoUV1GjvqY0GVziI+PJ0fOHAQF9eOJJx5Ld5t26NSb2TMnkjNnDhISEmjtH8T58xdsEucd7dq24o1WjfH0zEZ9X3/mzf2IKi+/RPbsXuz6Zh+du/Rh5Yr5ZMuWjYuXLuPn343r168DUKbMM/f08Zw5c9C2bUtcXGD0mMksX35/66Q+iDgXLZySKKa5c0Psru9mRnt26NSbH7/fm6gvjxk7JUPtefmPPxnQ7y28c3szdeo8ZsxceF/1u984U7sW9ez1Pj17BFCvbk0aNb61hm39erUccnwm7WsVK5bjubLVOXFsB2d+PcuF3y9y5tdzdAnsw9IlMyhcuCBRV6PxaxPEpUt/ALfetC1bOoty5Z7j4qU/WLJkBVu27GTihCHkzJWTNWs28uGgsf+5jg8iznVfhfD886UxzR8AyJsvD01eb0/FiuUS9bs5c0MyFOf3P/zEgP49SEiwsHDhMqZMnWfTOO+W3D0mo+PyQV9n77jfOP39m9Gvb3cSEixERp6kSOECdOjUmyaN69O5cxtu3IilW7f+/Hj6F4e+r6Q0Pt/u3ZVq1Srh5eXJjBkLmD1nsc37bdzlTF9Z0mrNhnA+X7GWyJMmFcuVoW+PzpQtbWT6z3XPX+K+2rNFi9d4f2AvoqNiiI6OwcvLk9p1WxEXd+tZoPCwW39o/PmXX/l86SwsCQmcOHmKLoH/rrtc26caA9/rRY0aVdi//zCjxgRTuFAB2rVtiXdubz74cAyrVq2/rzgf5HUI7p0bZXS+90XoGv68/B0FCpXj4sXLD6xeD9P4fJDtueDTyRjGU7i7uxM8eQ47duy2aX+940HEuX/fRsZPmMaOHbtZsng69X39nXJ8Thg3mBo1q+Dq6sq77w7Gy8vTOr+dP++TRDmBnDmyO837lfq+/ixcMDnR3H7Llp02nydwK0clKZhe+E2HT0h3/3Vxzf/rywAAIABJREFUlraxkvjiEAzDcAGymaZ5/fa2G7eekt8JHDRNc2oqr81oEv8xoDpQC3gRmEkWJPEdxYOeLNorxelcFKdzUZzORXE6F8XpXLI6iW8r95vEdxQPU79VnM5DcTqXhylOlMRPlZL4GafPiIijCABm307mw6018l2Biym/5D/7mltP35+480cDEREREREREREREVtIbk1xEXv0KfAMsNcwjCjAHegFvPSgf5BpmjGGYewBlj/oskVERERERERERB5mtlhT3tEpiS8OwTTNm0DfZA59nY7X3rOUzu39xZJsD73r+5Z3fd8hndUUEREREREREREReaC0nI6IiIiIiIiIiIiIiJ1SEl9ERERERERERERExE5pOR0RERERERERERERyRIWW1fAAelJfBERERERERERERERO6UkvoiIiIiIiIiIiIiInVISX0RERERERERERETETmlNfBERERERERERERHJEgkutq6B49GT+CIiIiIiIiIiIiIidkpJfBERERERERERERERO6UkvoiIiIiIiIiIiIiIndKa+CIiIiIiIiIiIiKSJRJsXQEHpCfxRURERERERERERETslJL4IiIiIiIiIiIiIiJ2Skl8ERERERERERERERE7pTXxRURERERERERERCRLaE38jNOT+CIiIiIiIiIiIiIidkpJfBERERERERERERERO6XldEREREREREREREQkS1hsXQEH5GKx6Ncm8oBoMImIiIiIiIiIiIutK2DPJhZ50+FzaH3PLM7SNtaT+CIP0CMeBW1dhUwXH3tWcToRxelcFKdzUZzORXE6F8XpXOJjzxJ3+bStq5Hp3POXeGjaU3E6D8XpXB6mOEUeNK2JLyIiIiIiIiIiIiJip/QkvoiIiIiIiIiIiIhkiQQtNpRhehJfRERERERERERERMROKYkvIiIiIiIiIiIiImKnlMQXEREREREREREREbFTWhNfRERERERERERERLJEgq0r4ID0JL6IiIiIiIiIiIiIiJ1SEl9ERERERERERERExE4piS8iIiIiIiIiIiIiYqe0Jr6IiIiIiIiIiIiIZAmLrSvggPQkvoiIiIiIiIiIiIiInVISX0RERERERERERETETimJLyIiIiIiIiIiIiJip7QmvoiIiIiIiIiIiIhkiQStip9hehJfRERERERERERERMROKYkvIiIiIiIiIiIiImKnlMQXEREREREREREREbFTWhNfRERERERERERERLJEgq0r4ID0JL6IiIiIiIiIiIiIiJ1SEl9ERERERERERERExE4piS9OwzCMtwzD2GMYxnbDMPYZhlHbMIwFhmE0SnLez4Zh5DQMo6ZhGJcMw9h219em2+cUvr0dYRjGF4ZhZMvMunt6ehIeFkp4WCgnju9gScgM67Ea1V/m8sWT1uPFihVOtazyL5RhV8Rafjtz2Lrvo0nD2L9vI9/sXEu+fHkzLY6Mqu1TjQP7N3H0yBbGjvkg1XNbtWrM6R/2EbJ4OgBdA9tZfyfRV0/zzDMls6LK6TZkcB/Cw0LZGr6C6zG/kCNHdgAmjBvMgf2bOHxoM9WrVU61jOzZvZgcPIroq6epV7cmAD6v3vqdHT+2nSZN6md2GGlK2i53eHvnYv3XS9izex3f7FzL44/nT7WcEiWK8tWaz4iPPUu2bLeGW3zsWWsb20OsKbWdq6srw4f1568/TAK7tE2znORi7dunm7Xsp54qllkhpEtKbZojR3bWf72E3d98za4Ix2/TXLlycixyG/Pmfmzdl1LsyXGE8ZnSNTYj9Uyufz//fGn27lnPschtBHVtn6kxpMfgQe/yw6k9hIeF8uWK+db9Pq9WI/LoVnZFrE2zDEeIM7kxBRAeFsqB/ZsIDwtlyOA+qZaRXJzz5n7M8WPbCQ8LZfq0cZkaQ3qk1G979+rC4UObOXpkCy1avJZqGY7QngA/nNrDroi1hIeF0jWw3T3HR454j/jYs2mWY89zv5o1qnDoYBh796xnxvTE/auhb21ion6iRvWXUy3jscceZdHCKcTHnsUwngLAz68pBw+EcSxyG5UrVcy0+meWM7+dI6jPIMpUbcCNG7G2rs5/1rhxPQ4f2szuXV/Rs0dAquf27BHA+bORjB41EIBChQqwY9sqjhwOZ8TwAVlR3QxJaU6QPbsX679eQsT21ezZvS7D782KFi1E1JUfrfMhW/bfO+Pz0MGwRNfbL1fMJzwslIMHwvhmZ+r30OTmQxm5L2WVtN6D/dfrrT21Jzwc/Tal99nwcN9XHJHFCb6ympL44hQMwygGdAGqmaZZA2gDDErHS7ebplnzrq+6t/cPB6aZplkN+AHolBn1vuP69ev41GlJg4ZtiIm5xoQJ05Icv4GLiwsXLl7m4sXL1K9Xi21bvmRXxNp73ojmzJmDcROmWrcrlC9LG//mVKnaiCFDJ+Dq6pKZoWSIxQLtO/Ska9d+tH2zJQP692Br+Ap2Razl5covJjo36mo0ny0OtW7Pmr0InzotmTMvhHXrw/nuux+yuvqpGjZ8Ej51WrIpbDtTp84nOjoGNzc3Tv/0CzVqvs7Onfto0qQBuXN7s2bVIjZtWMaqlQvw8PCwluHtnYtFi77g/PmL1n0TJw5h9JhgmrfoxJlffrNFaIkkbZc78uTxZubsRVR+2Rfv3N5UKF+WCuXLEh4WyvatKxk54r1E5+fNk5tRo4PvKSc6+houLi589933mRZDeiTXdnd4eHiwY8duDh2KtO7LSKyPP56f4cP608DXj5493yc21rZv5lNq03z58rB4yQpertKQXN45qVy5okO36eTgUaxfF55oX9LYHX18Jr3G3pG0nu7u7oQsns6mDcsI2/gF+fPns56bXP8ePqw/y5atooGvPydOfJelMaUk5to1PDw8OHr0hHWfq6sL06Z9at129DhTGlMA0VHReHl5cuToiQzHCRAdHYOnpydHjhzP1BjSI6V++88/V2jg689nn4Xi1/p1h2/PO65ejSJbtmwcO3Yy0X7fBj7cvHnTuu2oc7/GjeuxdcsuWrbqTLu2LXFzcwNuJXA7dmzN3r2HgFsJok0blrF5Uyjz532SqIx8+fIwYuS/f3B1dXVlSvAoAgP70L59T/76+++sC+gBuXI1iq4d/GxdjfvWqaMfISEraPx6e8aMfp8pk0cTtvELtm9dSalSJRKd+9PPZ1i3/t/7bt8+3Th85Dg1azVj1659WV31NKU0H3r66RJUqPA81Wu+TtTVaF55pVKGxidAXFw8cXHxxMRc46efz2RqHKnx8HCnS5c+dO3aj84Bbaz7mzXvhE+dlly8cInRYydneD4Eie9LtpbaPB7u73oL9tOe8HD02+TeZ4PuK/JwUBJfnEVuwBPwADBN8/vbyfz/qiaw5vb3a4Ha91W7dOrXtzv79h3m8F1voo8d/w7/Nt14tXYLChcqgL9fM4YM6UvuPN7Ex8fTsWPrRGVE7NzLP39fsW6XLFWcmJhrhCyexvsDe5M7t3dWhJIu4VsiMIySfLFsNtOmf8qI4QNwcXHB7RE3/PyaJjp33frwRJMrgDx5cjN29If06Ts0C2udfk89VYzALm0ZNmISADdv3mTGzIU0blyPxq/VZd78EPz9mvLSSy/g5uZKyZLFqfLyv3+8+P33ixw4eDRRmaVKFqd5s4YsXDCF8uXLZmk8yUmuXQDOnDnL5s07OHggjH/+vkLEzr3079+DAgWeJD4+nlYtG+Pl5Wk9/+ChSM6d/z1RGY2btKNFq87s2XuQDz94J9NjSU1ybXfH9evX2Rwekej8jMRavFgRYmPjGD9uMOPHDeLJ/z2R+QGlIqU2/fXXcyxdupKPJg0jJuYamzfvcNg27RrYjsjIkxw/YSbanzR2Rx+fd19jgyfPse5PWs86tavj28AHNzdXHnv8UerVq2U9N7n+XbJkcapVq8yypbOonsaTTllh6eer8PMP4rUm7ejzbjdr4ihs8w6uXbtuPc/R40xuTAF88OFY3vDryrDhk5gSPCrDcQZPnoOffxCdOr9N8CcjyJ7dK9NjSU1K/XbBwmU8V9qgV8/OTJkyz+HbE6BLYB/e8OvKos9CGTf232dPChZ8kp49AhIlGBx17rd48XL8/JoSHrac+Z9+zs2bN3Fzc2Pm9HH06TuUhIRb/9aue1AHSpUqgYsLVHn5RUqWLG4twzR/5PvvT1u3H3vsUfLkyU1QUHumTx9HqZIl7vm59q7Ms0/zeP5HbV2N+zZ16jxatHiNCeOH4OnpSecAf1xdXciRMwfNmvomOverr8ISbZcqWZxnnynF6pULaNiwTlZWO11Smg9FRn7LyZMmxyK3UbRoITZs2JKh8XnhwmXeaB1IfV8/rlyNSvMTDJlpU9h2/vjzL2bPnsi48YkTtm3aNCchIYGvvgrL8Hwo6X3J1lKbx9/v9dae2hMejn4L977P1n1FHhZK4otTME3zKLAP+On2EjqtDMN45D6KzGGa5o3b318EnrzvSqZg+LD+hIeFUqd2dXr2CGD6jE8THS9erDAFCv4PgJiYa7i6uoLFwiefzOa1Ju3o2fN9mjVrSHhYKG/3Dryn/PPnLhAVHU2rNwKJioqmZRofQc9KjRvX4/jx76hcpSFDh/TF1dWVDp1609qvK5OnzLX+bmrVrJrs6zt2aM3efYc4cybtjz7aQs8eAYSGruHq1SjrvoHv9aJXjwBeqd6YkydPYbHAt9+ewqdOSwIC3uHAwaOEh4Wy/uslyZZ57twFgoPnMHHSDPr27Z5VodwjrbYxjKd49dVXqPhiHa5ejaJTRz8sFgufL1tJfV9/ugT2sT7FndxSSvnz5+O55wxiY2OJioq51e9t5E6sSdsuNRmJ9dz530lISKBzl3c5eOjYPZPorJJWm7q5ubF65QK8vXNRs1YzYmKuOWybtn6jCa81qkP//m9Rr25NavtUS/Y8Rx2fd9x9jR054j3y5MkN3FtPi8XC5ct/4lOnJR069mLjxq2sXrmQ8LBQ8ubNc0+5589dYMnSlbz9ziDeH9grq8O6R5kyz+CdKxfXrl0nISEhxaeOHT3OlFSt+hIAUVHRuLq6ZjjOFyuWI1s2D6KiYnBxsf2n9VLqt2++2YLg4JE0aOjPtu3fOHR7Dh/Wn4jtq6la9SVu3IglKjo60TWx6esNyPdoXjauXwrcWgbCUed+Awf2YuKk6TxXtgYdO7xBkSIFqVC+LIWLFGT+3I8pV+45xo8fjAULW7ftwqdOSwK79sXFxYXwsFA+nX/vp0/+/PNvrl27xgcfjmH58rW81b2jDSITgL/++oe33nqPUaM/ITY2lvj4eBq+1pY323YnZMmXzJo5gfCwUJ59ttQ9rz13/gJhm7fTolVnugW158knbfsQwx1pzYd8fX3Imy8PZcrWYEfEHroFtc/Q+CxY8H8UL14UuHPdtt119/nnS7N5UyjDR3zEhImJl1/p26cb02csADI+H0p6X7KltObx93u9tZf2fJj6Ldz7Plv3FXlYuFgstljFRyRzGIbxLFAPeBO4CpwBQk3T/Oquc34GngNeAkKBuz/jt900zSGGYVw0TfPx2+eXBBaZplkljR9vecSj4H+u+/PPl2bnjjV457m1tnu7tq0oUqQA8+YvJXTZHCwWC1euXKVV60CqvVKJceMGceNGLPPmLWHW7EXWcvq8G4SfXzNKP1uK3bsP0DHgbYYO6cfTpUrg6eWJf5tunDr143+uZ3zsWe4nzrv5+zejX9/uJCRYiIw8yalTP9KqVWNu3Ijl3XcH883uA9ZzFy2cQpWXXyJ7di92fbOPlq26sDx0Lnv3Hrpn0vkgPIg49+/byPgJ09ixYzdLFk+nQ6fe/Pj9XiKPfcs/f//D/gNHGDN2CitXzCdbtmxcvHQZP/9uXL9+68nRMmWeIfjjEVSqVIFT359m+vQFXP7jTwb0ewvv3N5MnTqPGTMX2jTOpO0yd24IjRrVZdz4qSxbOouEhAS8snvh36YbOXNkZ86cj4iLi2PTpm0MHjLeWk6bNs3pHtSBSpUqEBGxhwHvjaRnzwBKPlUMF1dXOgW8nWbiPDPjfPLJJ/jlpwOJ2m7r1l00alSXYcMnsmzpLMqVe46Ll/5gyZIVrFsXnu5Y+w8YQZMm9alVsyq5vHPSrVt/dn2z3yZxQsptunPXXj5bOJWdO/disViYMy+EU+aPNmnTB3Udate2FW+0aoynZzbq+/ozb+5HiWLv3KWPQ4/PpNfYIoUL0KFTbypWLJeonnPmhrB0yQwKFy5I1NVo/NoEcenSH8CtP74k7d9btuxk4oQh5MyVkzVrNvLhoLE2jbNWzapMnDiU+Ph4tm3dxYsvlqNDp9706tGZBr4+FHjyCQ4fPkaDhm0IWTzNYeNMOqYqVizHc2Wr07xZI9q2bYmLC4weM5lVq9ZnqD2//+EnBvTvQUKChYULlzFl6jybxplSvz1xbAdnfj3Lhd8vcubXc3QJ7OPQ/RZuJYtq1KyCq6sr7747GC8vTxo1qkvPXu/f83Pq16tlk7nf/cbZosVrvD+wF9FRMURHx+Dl5Untuq2Ii4sDbq2dPXzER/z8y698vnQWloQETpw8RZfAf9fRru1TjYHv9aJGjSrs33+YUWOCKVyoAO3atsQ7tzcffDiGVavW/+c63okz7vLptE98QNZsCOfzFWuJPGlSsVwZ+vboTNnSRqb/XPf8JR7YPB7glar/x7RpY4mLi2fy5Ln83/+Vp1KlCty4foNOnd9J1O82bVhG6dJPExsbx9frNjN12jxmz5xIzlw5OXr0BJ0C3n5g9crM+dDIUR8TumwO8fHx5MiZg6CgfjzxxGPpHp8dOvW+FXfOHCQkJNDaP4jz5y/YJM51X4Xw/POlMc1by5LmzZeHJq+3559/rvLn5e8oUKgcFy9eJndu7wzNh3LmzJHovrR8edr/myYz40xtHn+/11t7ak94OPot3Ps+u76vv13eVwDbPx1hx4YWbePwCemhv4RkaRsriS9OwTAMFyCbaZrXb2+7Ad8BO4Fw0zQX33XuWdM0CxqGURPoYZpmi2TKOw08Z5rmNcMwagA9kzsviftK4juKB5nEt2eK07koTueiOJ2L4nQuitO5PExxZmUS31YedBLfXj1M/VZxOg/F6VyUxE+bkvgZp+V0xFkEALNvJ/Ph1hr5rsAXgN+dpXUMw/DjVmI/LZuB5re/bw5seLDVFREREREREREREUnb/awZLmJPPgWeAfYahhEFuAO9TNNcf3uJnQjDMG4AvwM97npdDcMwtiUpqx0wBFhkGEZX4Bfg/tZEEBEREREREREREfkPlMQXp2Ca5k2gbwrHPgI+Smb/NuCxVIqt80AqJyIiIiIiIiIiIgAkaLGhDNNyOiIiIiIiIiIiIiIidkpJfBERERERERERERERO6UkvoiIiIiIiIiIiIiIndKa+CIiIiIiIiIiIiKSJRKw2LoKDkdP4ouIiIiIiIiIiIiI2Ckl8UVERERERERERERE7JSS+CIiIiIiIiIiIiIidkpr4ouIiIiIiIiIiIhIltCK+BmnJ/FFREREREREREREROyUkvgiIiIiIiIiIiIiInZKy+mIiIiIiIiIiIiISJZIsHUFHJCexBcRERERERERERERsVNK4ouIiIiIiIiIiIiI2Ckl8UVERERERERERERE7JTWxBcRERERERERERGRLJGAxdZVcDh6El9ERERERERERERExE4piS8iIiIiIiIiIiIiYqeUxBcRERERERERERERsVMuFovWIBJ5QDSYRERERERERETExdYVsGf9i/k5fA5t/M9Ls7SN9Y9tRR6gRzwK2roKmS4+9qzidCKK07koTueiOJ2L4nQuitO5PExxxl0+betqZDr3/CUemvZUnM5DcTqX+Niztq6COCEtpyMiIiIiIiIiIiIiYqeUxBcRERERERERERERsVNaTkdEREREREREREREskSCrSvggPQkvoiIiIiIiIiIiIiInVISX0RERERERERERETETimJLyIiIiIiIiIiIiJip7QmvoiIiIiIiIiIiIhkiQQstq6Cw9GT+CIiIiIiIiIiIiIidkpJfBERERERERERERERO6UkvoiIiIiIiIiIiIiIndKa+CIiIiIiIiIiIiKSJbQifsbpSXwRERERERERERERETulJL6IiIiIiIiIiIiIiJ3ScjoiIiIiIiIiIiIikiUSbF0BB6Qn8UVERERERERERERE7JSS+CIiIiIiIiIiIiIidkpJfBERERERERERERERO6U18UVEREREREREREQkS1iw2LoKDkdP4ouIiIiIiIiIiIiI2Ckl8UVERERERERERERE7JSS+OI0DMN4yzCMPYZhbDcMY59hGLUNw1hgGEajJOf9bBhGTsMwahqGcckwjG23v/YZhjH8rvP63d631zAM38yuf6tWjTn9wz5CFk9PtD97di/Wf72EiO2r2bN7HcWKFU61nPIvlGFXxFp+O3MYgKJFCxF15UfCw0IJDwulcqWKmRZDepQoUZSv1nxGfOxZsmXLZt3fu1cXDh/azNEjW2jR4rVUy3B1dWX4sP789YdJYJe2AMyb+zHHj20nPCyU6dPGZWoM6VGoUAF2bFvFkcPhjBg+wLr/yxXzCQ8L5eCBML7ZuTbVMrJn92Jy8Ciir56mXt2aAPi8Wo0D+zdx/Nh2mjSpn5khpNukicPYv28jxyK3Ub1aZev+ypUqWvvd2V+P0DWwXarlJO27AB9NulX2NzvXki9f3kyLIT3Gjx3E/n0bOXxoM74NfKz7S5Yszp7d69i+dSWLFk5JsxyfV6sReXQruyJutb+bmxtLQmawf99G1n+9BHd390yLIS0pjc+hQ/py8EAYu7/5mmqvVEq1jOT6bXhYKAf2byI8LJQhg/tkZgjpUrNGFQ4dDOPQwTDGjvnAur9vn24cPBDGieM7/tN16PnnS7N3z3qORW4jqGv7TI0hPYYM7kN4WChbw1dwPeYXcuTIDsDsWRPZu2c9O3es4dlnS6VaxmOPPcqihVOIjz2LYTyFp6endVyfOL6DJSEzsiKUDEnpfpocR+ivd0sptkcfzcu2LV+yfetK1q5ehJeXZ6rl2PtcAaChb21ion6iRvWX7zn22aKp/HBqT5pl2PP19s51aO+e9cyY/u+8JaV7TXKSjk8AP7+mHDwQxrHIbXbRjknd7/jMyDzKlho3rsfhQ5vZvesrevYISPXcnj0COH82ktGjBgIpzyEd0ZnfzhHUZxBlqjbgxo1YW1cn3VKaD8Gt6+fff56iXdtWqZbhKPeXh/096P2+N7OnNk1pfpv0XpgaR3ifDQ/XfEjkbkrii1MwDKMY0AWoZppmDaANMCgdL91ummZN0zRrApWBqoZhVDMMozjQGngFaAR8ZBiGW6ZU/raoq9F8tjj0nv1PP12CChWep3rN14m6Gs0rr1Sifr1abNvyJbsi1t6TKMqZMwfjJkxNtC8uLp64uHhiYq7x089nMjOMNOXNk5tRo4Pv2f/PP1do4OvPZ5+F4tf6ddzd3QlZPJ1NG5YRtvEL8ufPZz3Xw8ODHTt2c+hQZKIyoqNj8PT05MiR45keR1r69unG4SPHqVmrGbt27bPub9a8Ez51WnLxwiVGj51M7tzerFm1iE0blrFq5QI8PDys53p752LRoi84f/6idd/EiUMYPSaY5i06ceaX37I0puS4u7tz/Pi3VKnaiIiIvTRo8Kr12J69B/Gp05Kg7gP448+/WByyPEN9t0L5srTxb06Vqo0YMnQCrq4uWRZXcgIC/Ano/A5ffvk1TZv++3e91xrV5dLFy9So1ZQXXihDtVcq0a5tK7Zt+ZLdu766548trq4uTJv2qXW7ceN6PPtsKV76v3pMn7kADw/bJZVSGp+9e3UhKKgfkz6ayeBBfTLcbwGio6Lx8vLkyNETmR5HWjw83OnSpQ9du/ajc0Ab6/7zv1+kTt1WLFr0Bc2a+mb4OjR8WH+WLVtFA19/Tpz4LktjSs6w4ZPwqdOSTWHbmTp1PtHRMeTLl5dOHf1o9NqbrFq9nnffCaJo0UJs2rCMzZtCmT/vk0Rl5MuXhxEjP7ZuX79+HZ86LWnQsA0xMdeYMGFaVoeVpqT3U0fvr3dLaa5QvfrL5Hs0LzVqNQWgefNGDj1XKFSoAB07tmbv3kP3HAvq2p6ffvq3fo56vW3cuB5bt+yiZavOtGvbEje3W9PMpPeajIxPV1dXpgSPIjCwD+3b9+Svv//O0pjS437HZ9J5lL3q1NGPkJAVNH69PWNGv8+UyaMJ2/gF27eupFSpEonO/ennM6xbH27dTmkO6YiuXI2iawc/W1cjw1KaD+XKlZPhw/qzfsMWwDnuLw/7e9D7fW8G9tOmKc1vk94LHf19Njw88yFnl+AEX1lNSXxxFrkBT8ADwDTN728n89PNNM0E4ABQCqgFrDdNM9Y0zUvAL0DpB1vlxNatD+fmzZv37I+M/JaTJ02ORW6jaNFCbNiwhSFD+v4/e/cdHkXV9nH8m8Q0Oojy0IvgKIIo6IugFJ9Ib9JJkA4hIEWpAgLSjaAQehcQohiQLiWEFpDeEZ9BQQUBKWIhCRBi9v0jyZoNSSCSZDfL73NduWCnnJx755yZM3dmz5I7Ty5iYmLo1KmNzfbhu/fz5x9/WV9fuXKd1m38qVvfl79uRtz3aaCMdvjICS5d/vWe5YsWL+e5sgZ9endl2rQF1HqjOvXr+eDm5soTTz5OnTqvW7e9ffs2W8PCbfYPmjoPX78AOnd9h6ApY8iWzTvDY0lNmdIlefaZMqxZtYgGDWrZrGvbtjmxsbGsXx+Kn29TXn75BdzcXClduiRVq7xk3e7XX69y6PDxe8pt3qwBixdN48UXy2dKLKm5e/cuny76gjmzJ1K/ng/Lgr+6Z5sZ0yYwdtxkIiOj0tR2S5cpSVTULZYtncHQIX3JnTtXhseTmlmzF7Nm1WJ69ujE0qUrrMuRRgU1AAAgAElEQVRXrFxHtmzerF29BE8PDwoWKkDgh+/j6eVJbKyFDu1tn9QK3bqLW7duW1+XKV0SFxcX1q/9jAD/9vc88ZWZUuqfY8dNZurUcbT1a0bBQgXS3G6Hvf8hrX27M2r0x0wLGpfhcdzPltCd/Hbjd+bOnUTgR/8M3JctW0m/d7vTu1cX5s9flubzUOnSJalW7RWWfz6H6sk8PWwPTz1VAv9u7Rg15mMAbtz4nSlT5rJq5ae84VOdQgUL0DOgI2XKlMLFBapWeYnSpUta9zfNs3z//bl7yh04oCcHDhzlqIPczCWW9Hqa1dtrYimNFcLCwvnx3Hk2b/yCQoULUqhggSw7VnBzc2P2zED6D/iA2Fjb26MKFZ6jZs2qLFgYbF2WVc+3S5euwNe3KWGhK1j46RfW45r0WpOW/vnEE4+TJ09uAgI6MHNmIGVKl7rn99rbw/ZPsB1HOarp0xfQokUjJn40Ei8vL7p28cPV1YXsObLTrKntB3yTxpHaGDKrKffs0zyZ/3F7VyPNUhoPzZg+gTFjJxMVdQtwjuvLo34PCg93b+ZIxzSl8W3Sa2FWv8+GR2M8JJIcJfHFKZimeRw4APwYP4VOK8MwHktLGYZh5ADqAEeA/wDXEq2+ChRMr/omNnrUIMJCQ3i95qvJrq9f34e8+fJQrnwNdoXvo0dAB7BYmDJlLo2atKd376E0a9aAsNAQ3unrf8/+hQv/h5IliwMQERFp96eZU/LWWy0IChpLvQZ+7Nj5DRaLhevXb+BTqyUdO/Vh8+btrFm1mLDQEPLmzXPP/i9VqoCnpwcREVG4uNg/xkuXrxC6dSctWnWlR0AHChYsYF03oH8PZs5aBIDFAt99dwafWi3p0uVdDh0+TlhoCBs3BCdf7qUrBAXNY9LHsxgwoGdmhJKi0aMGcejgFrp2aUvnLu8wZ+4SPhg5wGabii+Wp2zZp1mxYn3cgjS03cuXrhARGUmr1v5ERETS8j7Tm2SU0aMGcfhQKO8N7s1L/1ebwe+NZfj7/azrY2MtfDJ5Dm8264i7uzvnzv6MxWJhyJBxNGvRmZEfTKRnj46EhYbQps2b95R/6fIVfrlwiYaN21G0WGFq107T3x8zhXnmLM2ad2bDhq3x8aWt3b766stAwjnI/kOP558vy9YtIYwe8wkTJ8V9DNfNzQ3/bu0YPyGIgYNHExg4PM3nocuXrhD8+SreeXc4Q4f0yeywktW7VxdCQtZy82aEddmRYyepU68Nhw4f5+y5n7FgYfuOPfjUaol/9wG4uLgQFhrCpwvvfWIN4p7g6t2rCzNnfZrsentJ6Xqa1dsr3H+s4OrqytLgldSp14bIiEjOnvs5S44VEj7CX7xEURbOn0yFCs/x0UcjrOt927xJsaKFCF46i//85wn69wvIsufbIUP6MOnjmTxXvgadOramWLHC5MmTm4EDetpca9LSP2/c+INbt24x7P0JrFixjrd7drJDZMlLr/4JtuMoR/X773/y9tvvMW78FKKjo4mJiaFBo3a81a4ny4K/Ys7siYSFhiQ7pVlqY0ixn4IFC1CpUgUmjB9Kndo1GTTobfLnz5dlry+6B/3Hw9ybOdIxTW58m5ysfJ/9qIyHRFLiYrFY7F0HkXRjGMazxCXi3wJuAueBENM01yfa5ifgOeBlIAT4FnAj7gn8IaZpfmoYxlAg0jTNoPh9lgJLTNPcksqvtzzmUfhf133J4mlUrfIy2bJ5s+ebA8yfv4yGDWszdtxkQpbPIyYmhuw5shMQMJACBZ4gMHA4d+5Es2BBMHPmLrGW079fAL6+zSj7bBn27j1Ex859mTt7EjlyZCc2NpY2fgFcvnzlX9czJvoiDxNn27bN6RnQkcqVKxIevo9KlSrwXPnqfHtyF+cvXOTKr1c5f+ES3fz783nwLIoWLUzEzUh82wZw7dpvAOTPn4/ln8+hQoXnuHrtN4KDV/L9Dz8yeFAvYmMtLF68nGnTF/zrOqZHnIbxVNz7njMHx49/S4niRejQqQ9//nmTG9f/R6EiFbh69Tq5c+di1cqFeHp6cvXadXz9enD7dtyTEuXKPUPQ5DFUrlyRM9+fY+bMRVz/7QaDB75Nrty5mD59AbNmL7ZrnG5ubny2ZDolSxYjZ84cDB8RSFRkFA0b1qZ3n6H07tWFOrVr0rBx3JyKdeu8/sBtt1OXd/hg5ECeLlMKL28v/Nr24MyZs3aJE2DRp1MxjKdwd3cnaOo8XHChWLFCzJ6zhHVrluDq5saBA0fp3Wco7du1on//AO7ciWbc+CmsWbPJWs7EwBHUq+9DoYIFOHr0JE2bd2bJ4mnkfzwfFouFZi06W9t6ZseZUv9s0rguXbu25c6daHr0GMTZcz+nqd3myJGddu1a4uIC4ydMZcWKh5vH+GHj/Hr9Mp5/viym+QMAefPlocmbHXinb3eqVauMt7cXs2YtYu68pWk6D23btptJE0eSI2cO1q7dzPvDP7RrnAAHD2zmo4kz2LVrL8FLZ1K3vh9jRg+iVq2a3Iq6Rdt2cX8M/OLzOVhiY/n29Bm6+f8zp+sbPtUY8l4fatSoysGDRxk3IYjz5y+ye9dacuUp/VB1S5AecSaW9HratVt/u7bXBOkRZ0pjhQEDR7F29WJy5c7JhQsX8Wvbkzd8qtllrJCexzMsNC4ZUbx4UYoVK8TYcXHTyRQvXoSw0BWUfvqVLHu+bdGiEUOH9CEyIorIyCi8vb2oVac1c+dMsrnW7Nq1N039s2iRQrRv15JcuXMx7P0JrF698V/XMT3iTOph++eXIWttxlHpJb3jfO3V/2PGjA+5ezeGqVPn83//9yKVK1fkzu07dO76rs14Zsum5ZQt+zTR0XfZ8PVWps9YYDOG7NzlnXSrV0z0Re5ev/fTVRll7aYwvli5jhOnTSpVKMeAXl0pX9bI8N/rnr9UhoyHLly4BMTNEb5z517WrN2UpcdDkDXuQdMjzpSO6cPem6XnMc2o8W2fXl1troX1GrRl2dIZWfY+G7LOeAjQXwFS0atE6yyfkJ7+0/JMPcZK4otTMAzDBfA0TfN2/Gs34H/AbiDMNM2liba9aJpmYcMwagK9TNNsEb//XuBt0zQPG4bRETBM0xwSv892oLdpmqnNG/BQSfysIr1vchyV4nQuitO5KE7nojidi+J0LorTuWR2Et9eHjaJn1U8Su1WcTqPRylOlMRPVc8SrbJ8QnrmT19m6jF2jM8Iizy8LsDc+GQ8xM2R7wp8CfgmTK1jGIYvcYl9G6ZpWoB+wAzDMFyBbUADwzA8DMMoBBQGTmd8GCIiIiIiIiIiIiL/UBJfnMWnxM1bv98wjG3AGqCPaZobgTAg3DCMHUAToFdyBZim+Q1wDuhqmuZ5YB6wC1gJ9Ij/4lsRERERERERERGRTJOmL/4UcVSmaf4NDEhh3SfAJ8ks3wHsSLLML9H/pwHT0rOeIiIiIiIiIiIiImmhJL6IiIiIiIiIiIiIZIosPyG+HWg6HRERERERERERERERB6UkvoiIiIiIiIiIiIiIg1ISX0RERERERERERETEQWlOfBERERERERERERHJFLGaFT/N9CS+iIiIiIiIiIiIiIiDUhJfRERERERERERERMRBaTodEREREREREREREckUsfauQBakJ/FFRERERERERERERByUkvgiIiIiIiIiIiIiIg5KSXwREREREREREREREQelOfFFREREREREREREJFNYsNi7ClmOnsQXEREREREREREREXFQSuKLiIiIiIiIiIiIiDgoJfFFRERERERERERERByU5sQXERERERERERERkUwRa+8KZEF6El9ERERERERERERExEEpiS8iIiIiIiIiIiIi4qBcLBaLvesg4izUmURERERERERExMXeFXBknUu0yPI5tIU/rcjUY6w58UXS0WMehe1dhQwXE31RcToRxelcFKdzUZzORXE6F8XpXBSnc4mJvsjd6+fsXY0M556/1CNzPBWn83iU4pTUWfQcbJppOh0REREREREREREREQelJL6IiIiIiIiIiIiIiINSEl9ERERERERERERExEFpTnwRERERERERERERyRSx9q5AFqQn8UVEREREREREREREHJSS+CIiIiIiIiIiIiIiDkrT6YiIiIiIiIiIiIhIpoi1WOxdhSxHT+KLiIiIiIiIiIiIiDgoJfFFRERERERERERERByUkvgiIiIiIiIiIiIiIg5Kc+KLiIiIiIiIiIiISKbQjPhppyfxRUREREREREREREQclJL4IiIiIiIiIiIiIiIOSkl8EREREREREREREREHpTnxRURERERERERERCRTxGpW/DTTk/giIiIiIiIiIiIiIg5KSXwREREREREREREREQelJL6IiIiIiIiIiIiIiIPSnPgiIiIiIiIiIiIikiksmhM/zfQkvoiIiIiIiIiIiIiIg1ISXzKMYRglDMO4aRjGDsMwdhqGEWYYhk/8up8MwwiPX5fw89/4fQ4lKeeJRNv8YRjGwfj/+yfaZo5hGMeS7Lcj0bY74/8tm2i9n2EYhwzD2Bv/b/MU9k348cu4dws+njSKgwc2c/LEDqpXe8W6vHjxIkT8dZaw0BDCQkN4pXKlVMspVao469d+Rkz0RTw9PQGIib5o3b9Jk7oZGcZ9tWrVmHM/HGDZ0pn3rPvk47j34Jvd68iXL2+q5SQX59LPZrD3mw0cOriFlypVyJD6P6jk6gcwoH8PDh8K5dtTu2jRolGqZbi6ujJ61CB+/83Ev1s7ABbMn8ypkzsJCw1h5ozADI3hQaQUZ98+3ThyOJSjR7bSqWObVMvIls2bqUHjiLx5jjq1awIQFhrCoYNbCAsNYeSI/hkZwgNLqe3mypWTjRuC2bf3a77ZvY4nn8yfajmO3Edr1qjKkcOhHDkcyocThlmX9+3TjaNHtnL82DanaLcAEwNHcOjgFo4e2Wpzzk2u/qlx5OMJKbfbbNm82bghmPCda9i392tKlCiaajkvvlCOPeHr+OX8USDt16aMlNp5yJnabUr909muK0mldHyTk7S9t2/Xil/OH7W2Uy8vr8yo8r/yMHEmXIcOHwrl04VBmVHdB/aojIfg0T7fQly9/7hxhvbtWqVaRlYZ96XF+V8uEdB/OOVercedO9H2rk6a/HBmH3vC1xEWGkJ3//bW5c52D5rSuO+VypWsdbx44ZjNe5AcR+6fAHPnTGL/vo3s3rWWZ58tY12eXL9LTdI4IW336hktpXb7+ON52bHtK3ZuX8W6NUvw9k79uu/ocYokpSS+ZDTTNM2apmnWAPyBaYZhPB+/rl78uoSfbSkUcC1hG+AY0Cn+9VwAwzDcgUZAbsMwnkmye6dEv/8DYFr8PlWAd4HapmlWAf4LvJvwR4Yk+yb8BKfD+5Esd3d3Tp36jqqvNiQ8fD/16v3XZv3duzHcvRtDVNQtfvzpPBVfLE9YaAg7t69i7Jj3bLbNmyc348bfewMXGXkLFxcX/ve/7zMqjAcScTOSz5aG3LO84ovlaevXnKqvNmTkBxNxdXVJU5x58+ahVcvG1KnbhmPHTlG37usZHktqUjoOl3+9Sq3arViy5EuaNa2Pu7s7y5bOZMum5YRu/pL8+fNZt/Xw8GDXrr0cOXLCpozIyCi8vLw4duxUhsdxPynF2b59K0Z+MJFPJs/Bz7cZuXPnYu3qJWzZtJzVqxbh4eFh3TZXrpwsWfIlly9ftSkjMiISb28vjh3/NsPjeBAptd08eXIxe+4SXqlSn1y5c1HxxfJZto96eLjTrVt/uncfSNcuba3L//zzL+rV9+Ozz0LwbfNmlm+3bm5unPvxZ2rUfJPduw/QpEk967rk6p9Vjyek3G6ffroUFSs+T/WabxJxM5LXXqtM3Tqvs2PbV+wJX0dA9w422+fIkZ3AidNtliW9NtlLSsfA2dptSv3T2a4rSSU9voMH9WJ72Er2hK+jyisv2WybXHu/des2jz32GP8zz3Lnzp1MqfO/8TBxdunsR3T0XV6pUp/Vazbi5uaWafW+n0dlPASP9vk2Z84cjB41iI2b4m7nnGHclxZ/3Yyge0dfe1fjX7t5MwJPT09Onjxts9xZ7kFTG/ft238Yn1otCeg5mN9u/M7SZSuybP/Mly8vnTv50rDRW6xes5F+7wZY1yXX79ISZ3L36vaWXLutXr0K+R7PS43XmwLQvHnDLB+nSGJK4kumMU3zLDAOeDudi64LHAWCgdRGT/uBhD9H9wVGmqZ5I75ufwFDiUvsZ7q7d+/y6aIvmDN7IvXr+bAs+CvruitXrtO6jT916/vy180IevfqwqBBvShUqCAxMTG0atnY5i/Mh4+c4NLlX23Kb9ykPS1adWXf/sO8P8wuIVp9vTGMv//++57lpcuUJCrqFsuWzmDokL7kzp0rTXH+/vsfLP9yDQf3b6JundcJWbEuU+JJSXLHAWDZspX0e7c7vXt1Yf78ZdR6ozr16/ng5ubKE08+Tp06//zx4fbt22wNC7fZP2jqPHz9Aujc9R2CpowhWzbvDI8lNSnFOWfOEmbNCOTD8cNY8Gkwfr5NefnlF3Bzc6V06ZJUrfJPQuLXX69y6PBxm/2Hvf8hrX27M2r0x0wLGpfhcTyIlNru+fMX2bp1F4cPhfLnH38Rvnt/lu2jW0J38tuN35k7dxKBH/0zoF20eDnPlTXo07sr06YtyPLt9u+//2bW7MU0blyHxo1qs2DhMuu65OqfVY8npNxuT5z4jtOnTU6e2EHx4kXYtGkbI0cOIHeeXMTExNCpk+0naMJ37+fPP/6yvk7u2mQvKZ2HnK3dptQ/ne26klTi4/vYY26MGT0YFxcX3B5zw9e3qc22Sdt72LZw/Nr24I3arahX97+84VMtU+ueFg8TZ+nSJcmfPx9fr19Gm9ZvOlQS/1EZD8Gjfb6dMX0CY8ZOJirqFoBTjPvSotyzT/Nk/sftXY1/pZt/f1r7dmfJZyEEfjjcutyZ7kFTG/clmDFtAmPHTSYyMirL9s8bN35nypS5rFr5KW/4VKdQwQLWdcn1u7TEmdy9uj2l1G7DwsL58dx5Nm/8gkKFC1KoYIEsHaezi3WCn8ymJL5ktkNA2ftulTZ+wBfA50Bq83a0AI7E//8Z4hL/iR0DjHSu232NHjWIQwe30LVLWzp3eYc5c5fwwcgB1vWFC/+HkiWLAxAREYmrqwsWi4Uvlq+ibn0/uvn3tz4Vkfjj9Qny58/Hc88ZREdHExERhaurfbr96FGDCAsN4fWarya7/vKlK0RERtKqtT8REZG0bNEoTXGWL/8s9er+l2efq8bceZ8xcEB6/63o4bm5ueHfrR3jJwQxcPBoAgOHY7FYuH79Bj61WtKxUx82b97OmlWLCQsNIW/ePPeU8VKlCnh6ehAREYWLi+M+GTB2zGCavNkBv7Y9GDNqMBYLfPfdGXxqtaRLl3c5dPg4YaEhbNyQ/AdcXn31ZSChzdv3UnW/tmsYT/Hf/75GpZdqcfNmBJ07+WbJPgrw/PNl2bolhNFjPmHipH+mA3jrrRYEBY2lXgM/duz8Jku324TjOeS9PvTp1YXXqjfm9Okzqe6TFY/n/dpt/fo+5M2Xh3Lla7ArfB89AjqAxcKUKXNp1KQ9vXsPpVmzBoSFhvBOX/979k/u2uRonKndQvL981G6riTWsXNf2vh2Z+q0+am29WeM0uTPn4+7d+9y50603a8nafWgcV6+/CtHj52ibn1f6tSuSaWKzydTmuNwtnb7qJ9vCxYsQKVKFZgwfih1atdk0KC3yZ8/X5Yd9z0qRo8aRPjONbz66svcuRNNRKTte+9s96CpjfsqvliesmWfZsWK9XELsmD/TIjz2IlvqVOvDYcOH+fsuZ9T3ykNcSZ3r24P92u3rq6uLA1eSZ16bYiMiIx7D7JgnCIpcbFY9G3AkjEMwygBrDBN86VEy14CJgElgAtA4sdV6gEFku6TpMwdQC/TNE/Fv84O/AyUNE3zpmEYJ4COpmkeid82OxAJFAJ+jF932TCMo0Aj0zR/SVR2bmC/aZrPJNk3QSfTNH9MJWTLYx6FU39TUuDm5sZnS6ZTsmQxcubMwfARgURFRtGwYW3eGzKWFV/OJ0eO7MTGxtLGL4CC/3mSefM+4e7du2zZsoMRIz+yltW2bXN6BnSkcuWKhIfvY/B7Y+nduwulnyqBi6srnbu8c9+EVWpioi/yb+MEWLJ4GlWrvEy2bN7s+eYA8+cvo2HD2vTuM5SFC6bwdJlSeHl74de2BzmyZ3vgOEeM/IjBg3qRO3cuvLy9GDZsPFtCd9otzqT1q1SpAs+Vr847fbtTrVplvL29mDVrEXPnLeXz4FkULVqYiJuR+LYN4Nq134C4we/yz+dQocJzXL32G8HBK/n+hx8ZPKgXsbEWFi9ezrTpC/51HTMyzh4BHahVqyYWi4UVK9YxZ+5nrFq5EE9PT65eu46vXw9u374NQLlyzxA0eQyVK1fkzPfnmDlzETlyZKddu5a4uMD4CVNZ8ZCfrHjYOCHlthv40XSWfz6H2NhYvLN5p7ntpmcffdg4v16/jOefL4tp/gBA3nx5aPJmB749uYvzFy5y5dernL9wiW7+/bN0uy1YsAA//3iIEye/488//uTgoWNs376Hhg1rM2r0pHvq//XXYVnyeELK7XbsuMmELJ9HTEwM2XNkJyBgIAUKPEFg4HDu3IlmwYJg5sxdYi2nf78AfH2bUfbZMuzde4iOnfsyd/Ykm2vT5ctX7BJnSuchZ2u3KfVPZ7uuJJX0+G7bvptmzRpw5040/fqN4Ju9/3yVUtL2Pmr0xyxcMIWYu3f5/ocf6dipL+l17+NIcb7dawjLPptJzlw5uHb1Oi1b+1uvsQ9L46EH9yifby9cuATEfVfBzp17WbN2k93HfXevn3uoMtJi7aYwvli5jhOnTSpVKMeAXl0pXzbjnw1zz1/qodvtxMAR1KhZFVdXV/r1G4G3t5fD3YNm5Livd5+h9O7VhTq1a9Kwcdx3btSt83qW7J8AH04YRq1aNbkVdYu27XpSo3pVihUrxOo1m+7pd7/8cumB4+zU5R0+GDnQ5l79zJmzdoszpXY7YOAo1q5eTK7cOblw4SJ+bXvyhk81u8UJONZfXR1M6+JvZvmE9PKfV2fqMVYSXzJMCkn8AOKedm8KlDNNM+J++yRZvwPbJH5bYAbwU/wmTwJLTdMclHhbwzAaAt1M02wSv9/nwHLTNFcnKrsG0Nc0zWZJf88D+tdJ/KwkvW9aHZXidC6K07koTueiOJ2L4nQuitO5PEpxZmYS317SI4mfFTxK7VZxOg8l8e9PSfy002fVJNMYhvEU0A+YnI7F+gHtTNN8wTTNF4CqQEvDMGw6kmma6wEvwzAaxC8KAj4wDOOJ+LrlJG6+/vSsm4iIiIiIiIiIiCQSiyXL/2S2xzL9N8qjxoh/qt0TcAPeNk3zvGEYABsNw0g8nU4wsCXRPgkGmaZ5IJmCHweeBzYmLDNN8yfDMM4Rl8xP6l1glWEYYaZp7jMMYxiwyTCMaMAdCDJNM/G3Zn1qGEbi6XS2maY5+oEjFxEREREREREREXlISuJLhjFN8ycgZwrrSqSya7L7xO9XM9H/fwOKJrONT/x/ayZZfppEX1xrmuYGYMP9fo+IiIiIiIiIiIiIvSiJLyIiIiIiIiIiIiKZwmKH6WiyOs2JLyIiIiIiIiIiIiLioJTEFxERERERERERERFxUErii4iIiIiIiIiIiIg4KM2JLyIiIiIiIiIiIiKZItbeFciC9CS+iIiIiIiIiIiIiIiDUhJfRERERERERERERMRBKYkvIiIiIiIiIiIiIuKgNCe+iIiIiIiIiIiIiGQKi8Vi7ypkOXoSX0RERERERERERETEQSmJLyIiIiIiIiIiIiLioJTEFxERERERERERERFxUJoTX0REREREREREREQyRSyaEz+t9CS+iIiIiIiIiIiIiIiDUhJfRERERERERERERMRBaTodEREREREREREREZF0ZBjGZOAVwAL0NU3zYKJ1rwMTgL8BE+hqmmZsSmXpSXwRERERERERERERyRSxTvBzP4Zh1ADKmKZZBegCTE2yyVyghWmarwI5gbqplackvoiIiIiIiIiIiIhI+vEBVgOYpvkdkNcwjFyJ1lcyTfOX+P9fAx5PrTBNpyOSjmKiL9q7CplCcToXxelcFKdzUZzORXE6F8XpXBSnc3HPX8reVcgUj8rxVJzO5VGJUwT4D3A40etr8cv+AjBN8y8AwzAKArWB4akVpiS+SDp6zKOwvauQ4WKiLypOJ6I4nYvidC6K07koTueiOJ2L4nQuj1Kcd6+fs3c1Mpx7/lKPzPFUnM5Df6i4PwsWe1fBHlySLjAM40lgHdDTNM3fUttZSXwRERERERERERERkfRzibgn7xMUAi4nvIifWmcjMMw0zS33K0xz4ouIiIiIiIiIiIiIpJ8tQAsAwzAqApdM07yZaP3HwGTTNDc9SGF6El9EREREREREREREJJ2YpvmNYRiHDcP4BogF3jYMoyPwJ7AZaA+UMQyja/wuwaZpzk2pPCXxRURERERERERERCRTxD4ic+KbpvlekkXHE/3fMy1laTodEREREREREREREREHpSS+iIiIiIiIiIiIiIiDUhJfRERERERERERERMRBaU58EREREREREREREckUFsujMSd+etKT+CIiIiIiIiIiIiIiDkpJfBERERERERERERERB6UkvoiIiIiIiIiIiIiIg9Kc+CIiIiIiIiIiIiKSKWLtXYEsSE/ii4iIiIiIiIiIiIg4KCXxRUREREREREREREQclJL4IiIiIiIiIiIiIiIOSnPii4iIiIiIiIiIiEimsGCxdxWyHD2JLyIiIiIiIiIiIiLioJTEFxERERERERERERFxUErii4iIiIiIiIiIiIg4KM2JLxnGMIwSwEngMOACxADjTdMMMz2yUn8AACAASURBVAzjJ+AC8HeiXUYD54AVpmm+lKicJ4CQ+JcvAN8DkUCwaZpz47eZA1Q2TfOFRPvtALLHb+sCWICepmmejl/vB/QD7gLuwATTNFcm2t8L+BX4wDTNKQ/9hoiIiIiIiIiIiDziYjUnfprpSXzJaKZpmjVN06wB+APTDMN4Pn5dvfh1CT/bUijgWsI2wDGgU/zrhAS+O9AIyG0YxjNJdu+U6Pd/AEyL36cK8C5Q2zTNKsB/gXcNw/BJtG8D4pL4bR76XXgAb/hU49DBLRw/to0PJwyzLvf5bzVOHN/OnvB19y3D1dWV0aMG8ftvJv7d2gGwYP5kTp3cSVhoCDNnBGZY/R/UxMARHDq4haNHtlK92ivW5dmzZ2PjhmD2frOBPeHrePLJ/KmWU6pUcdav/YyY6It4enoCEBN9kbDQEMJCQ2jSpG6GxnE/j0qcvr5NOXwolJMndvBK5UrW5cWLFyHir7PWeiZel5zk4hzQv4f1PXzqqRIZGcZ9JVc/gA9GDuDwoVD2frOBaq9VTrWMbNm8mRo0jsib56hTuyYAYaEhHDq4hbDQEEaO6J+RITywVq0ac+6HAyxbOtNmeXocU0dpuzVrVOXI4VD279vIrJn/nBd/OLOPPeHrCAsNobt/+1TLSO54+vw37jx+6uROu/dNSLndpqXdZYXrCsDcOZPYv28ju3et5dlny1iXJ3ecUvPiC+XYE76OX84ftS775ONRHDywmW92ryNfvrwZUf0HllL/fKVyJWvfunjh2H3bb9I409q/M0OD+m8QFfEjNapXsS4rXbok+/Z+zc7tq1iyeNp9y0g6hnJzcyN42SwOHtjMxg3BuLu7Z1j97yel/jmgfw8OHwrl21O7aNGiUaplZJX+mdhHHw7n4IHNHD2ylfr1fFLd9oknHmfJ4mnERF/EMJ4CUh532FvCdeXI4VCbcfxXKxcSFhrC4UOhfLM79bF8Vh8nZMvmzcYNwYTvXMO+vV9TokTRVMtx9PNQSmMCZxoPpdX5Xy4R0H845V6tx5070fauTprkzJmDkyd2sGD+ZJvlyV1rkpPc+Sgt48bMktK1BeL63B83ztC+XatUy8gq56IEKY3rk9O7VxcuXzzB+HFDAChSpBC7dqzm2NEwxowenBnVFXkoSuJLpjFN8ywwDng7nYuuCxwFggHfVLbbDyTc2fcFRpqmeSO+bn8BQ4lL7CfwA0YChQ3DKJnOdb6HxQIdOvame/eBtHurpXW5q6sLM2Z8an3t7u7OsqUz2bJpOaGbvyR//nzWdR4eHuzatZcjR07YlB0ZGYWXlxfHjp3K6DBS5ebmxrkff6ZGzTfZvfsATZrUs67Lly8PS4NXUqVqA3LmysErr1Si4ovlCQsNYef2VYwd855NWXnz5Gbc+KB7fkdk5C1cXFz43/++z/B4UvKoxOnq6sq0oHH4+/enQ4fe/P7HHzbr796N4e7dGKKibvHjT+fTFOeTT+Zn9KhB1KvvS+/eQ4mOtu+NQkrHoW+fbgQEDOTjT2YzYnh/cufOxdrVS9iyaTmrVy3Cw8PDum2uXDlZsuRLLl++alNGZEQk3t5eHDv+bYbH8SAibkby2dKQZNc9zDFN4Ahtt3HjOmzftoeWrbrSvl1L3NzcrOtu3ozA09OTkydPp/l4Tpo0kvETgmjeojPnf/4lU2NKTkrHAGzbXVa+rgDky5eXzp18adjoLVav2Ui/dwOs65I7TnXrvM6ObV+xJ3wdAd072JSVI0d2AidOt76u+GJ52vo1p+qrDRn5wURcXV0yPqBUpNQ/9+0/jE+tlgT0HMxvN35n6bIVaYoT7u3f9lSkSCE6dWrD/v1HbJY3aliba1evU+P1przwQjmqvVaZ9u1asWPbV+zds/6eZFjSMVTjxnV49tkyvPx/dZg5exEeHvZL4qfUPy//epVatVuxZMmXNGtaP8v3z6S6dPGjS9d3+eqrDTRtWp/ixYuwZdNytm4JYeEC2w++5suXhzFj/0m43W/cYU8eHu5069af7t0H0rVLW+vyZs0741OrJVevXGP8h1Odepzw9NOlqFjxearXfJOIm5G89lrlLH0eAtsxQWLOMh5Kq79uRtC9Y2q3245ratA4Nn4dZrMs6bUmLeejBCm1EXtJqb3lzJmD0aMGsXFT3HOTznAuSpB0XB80ZSyhm79k5/ZVlClTymbbH386z9cb/2kHA/r34OixU9R8vRl79hzI7KqLpJmS+JLZDgFl07lMP+AL4HNSf2q+BZBwN/gMcYn/xI4BBoBhGLmA6sBaYPl9yk0XYdvCMYzSfLl8LkFT51mXh27dxa1bt62va71Rnfr1fHBzc+WJJx+nTp3Xretu377N1rBwm3KDps7D1y+Azl3fIWjKGLJl887oUFL0999/M2v2Yho3rkPjRrVZsHCZdd2FC5f4/PNVfPLxKKKibrF16y4GDepFoUIFiYmJoVXLxnh7e1m3P3zkBJcu/2pTfuMm7WnRqiv79h/m/WHvYi+PSpxPPPE4efLkJiCgAzNnBlKm9D+DpCtXrtO6jT916/vy180IevfqkqY4S5YoRnT0XT4KHMFHgcMp+J8CmRpbUskdB4Cx4yYzdeo42vo1o2ChAvj5NuXll1/Azc2V0qVLUrWKdWYwfv31KocOH7fZf9j7H9LatzujRn/MtKBxGR7Hg/h6Yxh///33Pcsf9piC47TdpUtX4OvblLDQFSz89AtrvN38+9PatztLPgsh8MPhaT6eZUqXpHmzBixeNI0XXyyfqTElJ6V2m7TdZeXrCsCNG78zZcpcVq38lDd8qlOo4D/ni+SO08iRA8idJxcxMTF06mR7eQ/fvZ8///jL+rp0mZJERd1i2dIZDB3Sl9y5c2VsMPeRUv9MMGPaBMaOm0xkZFSa4kyuf9uLm5sbs2cG0n/AB8TGxtqsW7FyHdmyebN29RI8PTwoWKgAgR++j6eXJ7GxFjq0t326MOkYqkzpkri4uLB+7WcE+Le/5ynFzJRS/1y2bCX93u1O715dmD9/WZbvn0nNmr2YNasW07NHJ5YuXUHPgI6UKVMKFxeoWuUlSpf+57kZ0zzL99+fs75Obdxhb1tCd/Lbjd+ZO3cSgR/ZJqbbtm1ObGws69eHOvU44cSJ7zh92uTkiR0UL16ETZu2ZdnzENw7JkjgTOOhtCr37NM8mf9xe1cjzbr7t+fEidOc+ta0LkvuWpOW8xGk3EbsKaVry4zpExgzdjJRUbcAnOJclCDxuH7v3sP4d3sLV1cXsufITrOm9W22Xb8+1OZ1mdIlefaZMqxZtYgGDWplZrUFsFgsWf4nsymJL5ktJ//Mg7/RMIwdiX7SfJdhGEZ2oBaw2jTNU8AdwzAqJtrk0/iyzwBvAT3il1sAN9vScElUt+bAZtM0b3H/J/zTRePGdTh16n+8UrUBY8e8R548uZPdzmKxcP36DXxqtaRjpz5s3rydNasWExYaQt68ee7Z/qVKFfD09CAiIgoXF/s9QTh61CDCQkMY8l4f+vTqwmvVG3P69Bnrejc3N9asWkSuXDmp+XozoqJuYbFY+GL5KurW96Obf3/rUy6JP6acIH/+fDz3nEF0dDQREVG4utrn9PYoxbniy/ncunWLYe9PYMWKdbzds5N1feHC/6FkyeIARERE4urqkqY4L13+ldjYWLp268fhIyfvufFzFOaZszRr3pkNG7Zy7uzPWCzw3Xdn8KnVki5d3uXQ4eOEhYawcUNwsvu/+urLQMJ7ZN9LckLbfb3mq8muf9hj6ihtF2DIkD5M+ngmz5WvQaeOrSlWrDDu7u68+urL3LkTTURk3PFI6/G8dOkKQUHzmPTxLAYM6JnJUT24pO0uq15X4J92e+zEt9Sp14ZDh49z9tzPqe9ksTBlylwaNWlP795DadasAWGhIbzT1/+eTS9fukJEZCStWvsTERFJy/tMb5JR7tc/Ie5TA2XLPs2KFevjFqQhzuT6tz0kTA1TvERRFs6fTIUKz/HRRyOs62NjLXwyeQ5vNuuIu7t7/HnXwpAh42jWojMjP5hIzx4dCQsNoU2bN+8p/9LlK/xy4RING7ejaLHC1K5dIzPDuy83Nzf8u7Vj/IQgBg4eTWDg8CzdPxMbPWoQhw+F8t7g3rz0f7UZ/N5Yhr/fDwsWtu/Yg0+tlvh3H4CLiwthoSF8uvDeJ0lv3PgjxXGHvT3/fFm2bglh9JhPmDjJdpqZAf17MHPWIgCnHifUr+9D3nx5KFe+BrvC99EjoEOWPQ+F71xzz5ggtXpm1fHQo6JN6yY0aliLQYPepk7tmrzhU42KL5anaLHCNteatJyPkhs3OqqCBQtQqVIFJowfSp3aNRk06G3y58+XZc9FSSUe11et+hLu7u40aNSOt9r1ZFnwV8yZPZGw0BCb6RYTXLp8hdCtO2nRqis9AjpQsKB9HxwTuR8Xe/zlQB4N8V9sm/RLagOIe9q9KVDONM2I++2TZP0OoFd8wh7DMNoCM4Cf4jd5ElhqmuagxNsahtEQ6GaaZpP4/T4HlpumuTpR2TWAvqZpNjMMIxR4Ckh4POQZoGLCl+KmwPKYR+HU35RU+Pk1Y+CAnsTGWjhx4jTFihaiY+e+9OnVlXr1fShUsABHj56kXoO2LFs6g6JFCxNxMxLftgFcu/YbEDcoXP75HCpUeI6r134jOHgl3//wI4MH9SI21sLixcuZNn3Bv64jxM3h+G/jLFiwAD//eIgTJ7/jzz/+5OChY2zfvoeGDWuze89+Pls8nd2792OxWJi3YBlnzLPMm/cJd+/eZcuWHYwY+ZG1rLZtm9MzoCOVK1ckPHwfg98bS+/eXSj9VAlcXF3p3OUdm+S54kz/OAF6BHSgfbuW5Mqdi2HvT+BW1C0aNqzNe0PGsuLL+eTIkZ3Y2Fja+AVQ8D9PPnCcgwaPoUmTurxe81Vy5spBjx6D2PPNQbvFmbR+lSpV4Lny1WnSuC5du7blzp1oevQYxNlzP7Nq5UI8PT25eu06vn49uH077inQcuWeIWjyGCpXrsiZ788xc+YicuTITrt2LXFxgfETprJixf2/+yIj4wRYsngaVau8TLZs3uz55gDz5y9Ll2Oanm33YeNs0aIRQ4f0ITIiisjIKLy9vXijdivGjx1CjZpVcXV1pV+/ERw/cTpNx/P6bzcYPPBtcuXOxfTpC5g1e/G/rmN6xJlSu23erKFNu1u9eiOfB8/KkteVBB9OGEatWjW5FXWLtu16UqN6VYoVK8TqNZvuOU6//HKJwMDh3LkTzYIFwcyZu8RaTv9+Afj6NqPss2XYu/cQnbq8wwcjB/J0mVJ4eXvh17YHZ86ctVucKfXP3n2G0rtXF+rUrknDxnFzo9et8/oDx9mxc1/mzp5k078vX75itzgThIXGJUWLFy9KsWKFmD1nCevWLMHVzY0DB47Su89Q2rdrRf/+Ady5E8248VNYs2aTdf+JgSNsxlBNm3dmyeJp5H88HxaLhWYtOlvbembHmVL/fKdvd6pVq4y3txezZi1i7rylWb5/Jrbo06kYxlO4u7sTNHUeu3bt5YvP52CJjeXb02fo5v/PXMtv+FRjyHt9qFGjKgcPHmXchCCKFilkM+5YvXpjutTrYeP8ev0ynn++LKb5AwB58+WhyZsd+PPPm9y4/j8KFanA1avXyZ07l9OOE8aOm0zI8nnExMSQPUd2AgIGUqDAE1n2PDQxcITNmMDb28shx0N3r5+7/4bpZO2mML5YuY4Tp00qVSjHgF5dKV/WyPDf656/VLqch9q3a0XrVo3x8vKkbn0/7t69C/xzrfnp5wtpOh/VqFbFpo3sCt/3UPVLj3ab0rXlwoVLQNz3puzcuZc1azfZ7VyU3teVxOP6i5d+5fr1G1SuXJE7t+/Queu7NuO2LZuWU7bs00RH32XD11uZPmNB3HknZw6OH/+Wzl3eSbd6xURfhLgHRSUFPkVqZ/mEdNgvWzL1GCuJLxkmaULeiPsGmI3AG8Au0ieJvwGYbZrmukT7bwdKxf+beNvNwFTTNDcYhvEKMBuoZZrmNcMwcsbXbQjwPXAYKG6aZkz8viMAd9M0U/uc3EMl8bOK9L7oOirF6VwUp3NRnM5FcToXxelcFKdzUZzOJbOT+PaSXkl8R/cotdtHJU6UxE+Vkvhp91hm/jJ5JBnxiXdP4qaveds0zfOGYUDcdDqJJ1QMBrYk2ifBINM07/mWEcMwHgeeJy75DoBpmj8ZhnEOqJpMXd4FVhmGEWaa5j7DMIYBmwzDiAbcgSDTNMMNw+gLfJ6QwI+3OL5ujjHZnYiIiIiIiIiISBYUS5bP4Wc6JfElw5im+RNxc+Ant65EKrsmu0/8fjUT/f83oGgy2/jE/7dmkuWnif/i2vjXG4ANyex/z6R3pmn+nHhfERERERERERERkczgWN9IISIiIiIiIiIiIiIiVkrii4iIiIiIiIiIiIg4KE2nIyIiIiIiIiIiIiKZwqI58dNMT+KLiIiIiIiIiIiIiDgoJfFFRERERERERERERByUkvgiIiIiIiIiIiIiIg5Kc+KLiIiIiIiIiIiISKaItWhO/LTSk/giIiIiIiIiIiIiIg5KSXwREREREREREREREQelJL6IiIiIiIiIiIiIiIPSnPgiIiIiIiIiIiIikik0I37a6Ul8EREREREREREREREHpSS+iIiIiIiIiIiIiIiDUhJfRERERERERERERMRBaU58EREREREREREREckUsZoVP830JL6IiIiIiIiIiIiIiINSEl9ERERERERERERExEFpOh0RERERERERERERyRSaTift9CS+iIiIiIiIiIiIiIiDcrFY9JcPkXSiziQiIiIiIiIiIi72roAjq1L49SyfQ9t7cXumHmNNpyOSjh7zKGzvKmS4mOiLitOJKE7nojidi+J0LorTuShO56I4nYvidC4x0Re5e/2cvauR4dzzl3pkjuejEqdIelMSX0REREREREREREQyhWaGSTvNiS8iIiIiIiIiIiIi4qCUxBcRERERERERERERcVBK4ouIiIiIiIiIiIiIOCjNiS8iIiIiIiIiIiIimSIWzYmfVnoSX0RERERERERERETEQSmJLyIiIiIiIiIiIiLioJTEFxERERERERERERFxUJoTX0REREREREREREQyhUVz4qeZnsQXEREREREREREREXFQSuKLiIiIiIiIiIiIiDgoJfFFRERERERERERERByU5sQXERERERERERERkUxhsWhO/LTSk/giIiIiIiIiIiIiIg5KSXwREREREREREREREQel6XREREREREREREREJFPEoul00kpP4ouIiIiIiIiIiIiIOCgl8UVEREREREREREREHJSS+CIiIiIiIiIiIiIiDkpz4otTMQyjBLACmA6UM01zQJL1O4BepmmeSrqPaZovxb/2AcYALoAHMMc0zbmZUX8RERERERERERFnZrFoTvy00pP4IokYhlGMuD8A+JqmWQWoAtQxDKNTRv5eLy8vwkJDCAsN4dtTuwheNsu6zs3NjeBlszh4YDMbNwTj7u6ealkvvlCOPeHr+OX8UQCKFy9CxF9nreW/UrlSRoZyX6VKFWf92s+Iib6Ip6endbnPf6tx4vh29oSvu28Zrq6ujB41iN9/M/Hv1s66vEiRQvxy/igjhvfLkLqnVc6cOTh5YgcL5k+2LvP1bcrhQ6GcPLHjvsciWzZvpgaNI/LmOerUrgnEvU+HDm7h1MmdNGlSNyOr/8BatWrMuR8OsGzpzGTXjx3zHjHRF+9bjqO33cSSO7bJ6d2rC5cvnmD8uCEAjBjejx/O7CMsNISvVi7MjKqmyRs+ce3r+LFtfDhhmHV5Wtpdcv1zwfzJnDq5k7DQEGbOCMzQGB5ESnH27dONI4dDOXpkK506tkm1jKzeP7Nnz8bGDcHs/WYDe8LX8eST+VMtJ7lzd0z0RWv/tHe8KZ1bs2XzZuOGYMJ3rmHf3q8pUaJoquU48nkotbFC6dIl2bf3a3ZuX8WSxdPuW1bSa25axxoZKaVxwlcrFxIWGsLhQ6F8szv1sUJy/TMsNIRDB7cQFhrCyBH9MzKENGlQ/w2iIn6kRvUq1mXONPYbOaI/YaEhbA9bye2on8mePZt1XXKxJ+eJJx5nyeJpxERfxDCeAtI2nsosEwNHcOjgFo4e2Ur1aq/cs94ZxkOPUv9M6Xi+UrmS9VhcvHCM7v7tUy3HkY8npDymfZj++cOZfewJX0dYaMh93x9HdP6XSwT0H065V+tx5060vavzwFK6z3C2cTyknlNwtlhFElMSX8RWD2CaaZo/A5imGQ28C2RoVvj27dv41GpJvQZtiYq6xcSJM6zrGjeuw7PPluHl/6vDzNmL8PBwp26d19mx7Sv2hK8joHsHm7Jy5MhO4MTpNsvu3o3h7t0YoqJu8eNP5zMylPvKmyc348YH3bPc1dWFGTM+tb52d3dn2dKZbNm0nNDNX5I/fz7rOg8PD3bt2suRIyesy9zc3Jg2dRxr1m7O2ADSYGrQODZ+HWZ97erqyrSgcfj796dDh978/scf5M6di7Wrl7Bl03JWr1qEh4eHdftcuXKyZMmXXL581bps0qSRjJ8QRPMWnTn/8y+ZGk9KIm5G8tnSkGTX1a/nw99//219nZXbbmKJj23QlLGEbv6SndtXUaZMKZvtfvzpPF9vDLNZFnXrFh4eHhw//m2m1fdBWSzQoWNvuncfSLu3WlqXJ213ae2fAJGRUXh5eXHs2CnsLaU427dvxcgPJvLJ5Dn4+TZz6v6ZL18elgavpErVBuTMlYNXXqlExRfLExYaws7tqxg75j2b7VM6d0dG3sLFxYX//e/7DIvhfpI7tyZ4+ulSVKz4PNVrvknEzUhee61ylj0PpTZWaNSwNteuXqfG60154YVyVHutMu3btWLHtq/Yu2f9PTeySa+5yY017CWlttaseWd8arXk6pVrjP9wapr7J0BkRCTe3l4cc5Dzb5EihejUqQ379x+xWe5MY79Roz/Gp1ZLtoTuZPr0hURGRgH3xl68eBG2bFrO1i0hLFwwxaaMfPnyMGbsPwnG1Pq8vbi5uXHux5+pUfNNdu8+QJMm9WzWO8t46FHpn6kdz337D+NTqyUBPQfz243fWbpsRZY9nnDv/Qo8XP9McPNmBJ6enpw8eTrjKp9B/roZQfeOvvauxr+S3H2Gs43jIeVzkTPGKpKYkvgitp4BjiZeYJrmeSC/YRgZ3l8GDujJgQNHOZroglGmdElcXFxYv/YzAvzb4+npyciRA8idJxcxMTF06mT7tGj47v38+cdf1tdXrlyndRt/6tb35a+bEfTu1SWjw0jV4SMnuHT513uWh27dxa1bt62va71Rnfr1fHBzc+WJJx+nTp3Xretu377N1rBwm/3HjhnMks9CuHLF9obAXrr7t+fEidOc+ta0LnviicfJkyc3AQEdmDkzkDKlS+Hn25SXX34BNzdXSpcuSdUqL1m3//XXqxw6fNym3DKlS9K8WQMWL5rGiy+Wz7R4UvP1xjCbG9MEhQsXpHevLjYD+6zcdhMkPrbZs2fDv9tbuLq6kD1Hdpo1rW+z7fr1oTavP/9iNb5+ATRq0p7+/Xrck/S3t7Bt4RhGab5cPpegqfOsy5O2u7T2z6Cp8/D1C6Bz13cImjKGbNm8My2m5KQU55w5S5g1I5APxw9jwafBTt0/L1y4xOefr+KTj0cRFXWLrVt3MWhQLwoVKkhMTAytWjbG29vLun1y5+7GTdrTolVX9u0/zPvD3s3wWFKS3Lk1wYkT33H6tMnJEzsoXrwImzZty/LnoeTGCitWriNbNm/Wrl6Cp4cHBQsVIPDD9/H08iQ21kKH9q1sykh6zU1urGEvKY0TANq2bU5sbCzr14emuX8Oe/9DWvt2Z9Toj5kWNC5DY3gQbm5uzJ4ZSP8BHxAbG2uzztnGfk89VQL/bu0YNeZjIPnYewZ0pEyZUri4QNUqL1G6dEnr/qZ5lu+/P2d9nVqft5e///6bWbMX07hxHRo3qs2Chcus65xpPPSo9M/UjmeCGdMmMHbcZCIjo7Ls8UzufuVh+ydAN//+tPbtzpLPQgj8cHjmBJOOyj37NE/mf9ze1UizlO4znG0cDymfi5wxVpHElMQXseUKuKWwPEOMHjWIsNAQar1Rnd69ujBz1qc26y9dvsIvFy7RsHE7ihYrTO3aNcBiYcqUuTRq0p7evYfSrFkDwkJDeKev/z3lFy78H0qWLA5AREQkrq4uGRVKurJYLFy/fgOfWi3p2KkPmzdvZ82qxYSFhpA3b557tm/6Zn169ez0/+zdd3wVxfrH8U8SQicgYqGJDUcpcgX8oaAU6YioCIGES0cICBZAsNJRERQpojQFpIihiKggIbSIoIAoIDoodiyIXpUUUsz5/XFOQhJSiElOy/d9X75uzu5mMw+7Mzv7nNlZ+vQOpU/vUOrVu9YDpT6rZ487uaNzW8aMuY/27VrSpvWt/PHHnyQkJPD4E0+zZs1G7hvWH4cDPv/8GK3bdmfgwIfYf+BToqMi2fTOymz3+9NPvzJr1kJmPPcSo0cPc3NUmaWdu61aNst2/d13daTyhRfw3qZVgPOxZH84dzMe2+a33kRKSgq339Gb//YexoqV65j/8nSioyK57rra5/xuvXrXElKhAgkJZ0hNTfWamNJ06dKeI0e+4KamtzNl8iNUqlQROPe8y2/9bNyoAaVKlSQ2Np6AAM/HnFOcUyaP5c67+hLeayiTJ4716/oZFBTEhvVLCAmpQMtWXYmPT8DhcPD66vV06BTOvYNHpY/MzzjlUJoqVSpTt64hKSmJ2Nh4AgM906WcNHEMa95YdE7bmqZTp9ZcULkS9eq3YFfMXoZG9PXJdiivvkJqqoPnZ87nrq79CA4O5uvj3+FwOHj00al07TaA8ROmM2xoP6KjIunZ865z9p9tX8MLjR41lHkvLQHId/1s1uxG3UWkRgAAIABJREFUIO04evYWKO3x/VqX1+SVRTNp0KAuzz47Ln29v/X9RgwfSGTkW5w+HQtAwxvqU/Oy6plid+Bg+47dtG7bncFDRhMQEEB0VCSvvnLuSMvs+lOelFY/H33kfu4fPpBbmnfh6NFj6ev9tT+UlT/Vz9yOJzjP4Tp1rmHNmredC3z0eGZ3v1LQ+hkcHEyzZjeSmJhEbJznj2dxktN9hr/143NTnGL1B6k4fP4/dwvQiwTEnxT0xbbGmGnAz9baFzKsrwW8Za1tkMefd5QoWf1fl/366+vw/q63CKl0NQB9eody2WXVmPHcy7y+6mWqXFgZh8NB124DaNTweqZNe5LExCQWL17J/AXL0vczamQEYWFdqXNdbfbs2U+/AQ+w4OUZlC9fjtTUVHqGR/Dzz7/+63KmJJ2gIHH26nUPwyL60aRJQ2Ji9tKoUQPq1m/O/cMH0bFTa6pVvYSDBw/T8fZerFj+IjVrVif2dBxhvSL47bffAWfyaPWq+TRoUJeTv/3OypVrmTLVecjS5sOfNPn5f13GwogzTZ/eofQI7ULp0qXo0CmcQQPD6dO7OyEVQ3j8iafZvn0369e+QqlSpTj52ynCwody5oxzdGS9etcya+ZkmjRpyLEvv2bevCWc+v0Pxj58HyEVQ5g7dzEvvbzU43EuWzqHpjffSNmyZdj9wUcsWrSCzp3bMeL+x875Ox3at/LIuVtYxzOjPr1DadHiZhISztCkSUMSzyQyYNBDHDt2PH2bLZtXU6fONSQlJfPOu1tZt+4dZsyYQEpKCju272bso1MKtUwFjTM8vCsPjx5GaqqDQ4eOclnNavQb8ACNGjXIdN4tXLSCVStfOu/6+eVX3zB2zHBSUx0sXbqaOXMXe2WcQyP60rZtSxwOB2vWbGT+gtf8tn6+v/tDXls6l/ff/xCHw8HCxSs4Zo+zcOHzJCcns2XLDsaNfzZ9P1nb7rGPTGHEiIFcfdXlBAQGMmDgg+ckOdwZ59CIvpna1oT4BDp3bseUqTOJXL2QlJQUypUvR0TEw1xyyUU+2w7l1Fd4ef4yNm5YRmBQEB99dJAR9z9Gn96hjBoVQWJiElOfeoENGzan72f6tHGZrrl33zOAZUvnZOprpNVpd8eZUz/hr79O88epL6hWowEnT56iYsWQfNXP8uXL0bt3dwIC4KmnZ7NmTd7v4CnKODOKjopk0uTnqVWrptf1/Qojzn0fvcez019k1649rFw+jw6dwklOTs4U+7ff/cDrq+bjSE3ls6PHuHfw2XnR27S+lUcfuZ8WLZqyb99Bpj49i5o1qmWq82++ualAZSxonFWrXsJ33+zn0OHP+evPv9i3/xO2b9/td/2h4lI/8zqeI4YPpH27lnTu4pxH21ePZ5qs9ysFrZ8tbr2ZFi2bEhgYyMiR49gVs7dA5UtJOkHyqa/z3rCQvLU5mtfXbuTQUUujBvUYPXwQ9euYIv+7wVWuLNDxbNWyWab7jMaNG/hlPx5ybou8KVbXO1D0TUAuGlza1OcT0p/+8oFbj7GS+OJXCiGJXxvYArSx1h43xgQDrwMbrbVL8vjzBUri+4qiSIZ6I8XpXxSnf1Gc/kVx+hfF6V8Up39RnP6lOMXpziS+pxQ0ie8ritN5i5L4uVISP/9KuPOPibhZD2NM4wyf27n+/1VjTJzr521A+lAJa+2XxpgI4C1jzBmcU+ssOY8EvoiIiIiIiIiIiEihUxJf/Iq19lsgLXG/JJtNWubwq+nJfmvte0BdY8xjQKWMU+uIiIiIiIiIiIjIv+fwwJzyvk5vGRHJ2RygkTEmxhhT9JPgiYiIiIiIiIiIiGShkfgiObDWngZae7ocIiIiIiIiIiIiUnxpJL6IiIiIiIiIiIiIiJfSSHwRERERERERERERcYtUh+bEzy+NxBcRERERERERERER8VJK4ouIiIiIiIiIiIiIeCkl8UVEREREREREREREvJTmxBcRERERERERERERt3CgOfHzSyPxRURERERERERERES8lJL4IiIiIiIiIiIiIiJeStPpiIiIiIiIiIiIiIhbpDo0nU5+aSS+iIiIiIiIiIiIiIiXUhJfRERERERERERERMRLKYkvIiIiIiIiIiIiIuKlNCe+iIiIiIiIiIiIiLiFA82Jn18aiS8iIiIiIiIiIiIi4qWUxBcRERERERERERER8VJK4ouIiIiIiIiIiIiIeCnNiS8iIiIiIiIiIiIibpHq0Jz4+RXg0D+aSGFRZRIRERERERERkQBPF8CbXXNRY5/PoR37bb9bj7FG4osUohIlq3u6CEUuJemE4vQjitO/KE7/ojj9i+L0L4rTvyhO/6I4/UtxijP51NeeLkaRC65yZbE5niKFTXPii4iIiIiIiIiIiIh4KY3EFxERERERERERERG3cGhG6nzTSHwRERERERERERERES+lJL6IiIiIiIiIiIiIiJdSEl9ERERERERERERExEtpTnwRERERERERERERcYtUh+bEzy+NxBcRERERERERERER8VJK4ouIiIiIiIiIiIiIeCkl8UVEREREREREREREvJTmxBcRERERERERERERt3CgOfHzSyPxRURERERERERERES8lJL4IiIiIiIiIiIiIiJeStPpiIiIiIiIiIiIiIhbOBypni6Cz9FIfBERERERERERERERL6UkvoiIiIiIiIiIiIiIl1ISX0RERERERERERETES2lOfBERERERERERERFxi1Qcni6Cz9FIfBERERERERERERERL6UkvoiIiIiIiIiIiIiIl9J0OuJTjDFhwDKgKhAARLpW/Qf4EogDVgJJwGTguGt9OWCxtfZl137qARuAmdbaua5lE4Ce1tprM/y9esBhoJW1dkdRxiYiIiIiIiIiIiKSlUbii68Jx5mY72at/c1a29Ja2xL4BOjv+rzAte3qDOtvAe43xlxujCkHzAGis9l/sDHmhgyfewJfF1UwaUqXLk10VCTRUZF8dmQXK1e8lL6uRo1qxOzcwEcfbmbzu6soXbp0rvu64T/12B2zkR+/PwhArVo1iP37ePr+b2rSqEhjOR8VKpTn8KEdLF40E8g9/uxcdNGFLFs6h5SkExhzFQBfHdvL7piNREdFMmRwnyKP4XyEhnbh668+YsXyeZmWlytXlk3vrGTPB++wO2YjF19cJdf9XHllLd5+6zVSkk5QqlSp9OVDI/qSEPcttWrVKJLy51fLFk35+EAUHx+I4pmnH89126z/Nn16h/Lj9wfTz4O8znN3y+kYtL7tVg59up3dMRvz3EdgYCCTJo7hf79bBt/bO315jRrV+PH7g4x7cmSRlD0/cquLVateQszODezd8y6b3lnpF21RmudmTGTfR+9x+NAOmt96U67bli1bhtmzphJ3+mvat2sJQHRUJPv3bSE6KpLx40a5ocTnpyivLQDPP+f8d/vg/Y1UrnxBkcWRl5zq5wP338vBj7fy6Sfb6Nbtjlz3kV39XLxoJkcO7yQ6KpJ5L04r0hjOR05xjh41lAP7o/jsyK5/FSd4VzuU07UkP/XMF45nmts7tSE+9htaNL85fZm/9f1y66Plpx3Jrg6kJJ1Ij/POOzsUWQznI6d+X9myZdj0zsr0a+jll9fMdT/efDxzqp8F7Q95Y/1cMH8GH+7dxPu73uK662qnL8+uH5AbX7x+grPcf/5xjD69Q3Pdhy/0i8Y9OZKvju0lOiqSdWtfSV/e+rZb2b9vC0cO78yz/fCV8zY/vv/xJyJGPUm9Zh1JTEzydHHy5dlnnmTfR+9x8OOtdOrYOn35pZdezO6Yjeza8Sbbo9dSsWJIrvvJ2na1aH4zp04eTW9v82qvpWAcDofP/+duSuKLzzDGVAb+DxgFhOXnd621iThH1F8JJAKdgJ+y2fRdnF8UpOkA7P035c2PM2fO0Lptdzre3ov4+ASmT38xfV1ISHkmTppB02adadKkIZdfXoMO7VuxY9s6dsdsJGJI30z7Kl++HNOmz820LDk5heTkFOLjE/jm2++LOpw8zZ41lU3vnv0OJbv4a9WqwZbNq9m6JZJXFr+Q6fcrV67E5Ckzz9nv6dOxlCpVisOHjxZ5DOcj9nQcry2PPGd55cqVWL5yLTc3vZ0KIeW56aZGNLyhPtFRkezcvp4pkx/JtP0FlSoy9alZmZb95z91ueGG+pw48UuRxpAfJUsGc++9oxgy5GEGDezF2DHD2R69lt0xG7n5psaZts3u3yYh4QwlSpTgC3ucxMREdxY9T9kdA4DAwABefPHV9M/BwcGsWD6PLZtXE/XeG1SpUjl9XcmSJdm1aw8ff3wofVlQUBBzZk9lw1vvFW0A5ym3tqhy5Uo8O+PF9LbImKt8vi0C5zE7cuRzmjbrTEzMh3TseBsVK4bw1pvL2LJ5NW+uX0LJkiXTtw8JqcCyZW/w888nM+0nLjaOMmVK88mnn7k7hBwV5bWl4Q316RV+D02bdWb8hOkEBga4La6scqqff/31Nx07hfPaa5GE9bwr3/UTIC4untKlS/PJJ0eKPI685BTnz7+cpG27UJYte4Oud3fy+XYo67Uko4z1zNePJziT9f379+TDDz/OtNwf+37Z9dGya0fy2x8CiItLICAggC+++LLI48hNTv2+a665koYNr6d5y7uIPR3HLbc08dnjmVP9LGh/CLyrflaufAED+ofR+Y7/8uaGTYx8KCJ9XXb9AH+7flaoUJ5JE8ewafM2AL/oF8UnJFCyZEk+zVCeGTPG89TTs7in2wC+/+5Hnz9v8+vv07EM6ZevtIrXGDgwnIGDHmLdune4++5O6cubNGlIyVIlad7yLqpXr8r19a+jT+9Qdmxbx57db5/zZU3WtgvgzJlEAgIC+PXkKU6ePOWWeETOl5L44ku6A28Dm4Haxpjq5/uLxphLcH4BcMRam2KtTchh001AZ2NMgDGmMfAFkFzAcp+3h0cP46OPDnIwQyfg6NFjfPPtD3z6yTa279jNsWNfM378aCpWCiElJYX+/Xtm2kfM+x/y159/p3/+9ddT9Og5mA6dwvj7dCwjhg90VzjZGjK4D4cOHeXIZ/acdRnjHxbRj9q1ryQgAJre3Jirr74ifTtrj/Pll5kfkLh38Ch6hA1h2WuRTHvmySKP43y8uymaf/7555zlP/zwE6tWref55yYSH5/A1q27GDNmONWqVSUlJYXQ7l0oU+bsqLsDHx/ip5/PJuvLly/H9GnjeXjMJLfEcb62RO3k9z/+x4IFM5g1eyGTJ40lICCAoBJBhIXdnWnbrP820dtiCO81lDbtQunY4TbatL7V3cXPVdZjkCZq6y4SEs6kf27bpjmdOrYmKCiQiy6+kPbtW6WvO3PmDFujYzL9/pTJY1n2WiS//pr5xsfTsmuLPvvMsnXrLlYsn0dMzIccOnTUp9uiNMnJyby65HXmvzydTh1bs2LlOsLD7ubGG/9DUFAgV199BU1vPvsl1C+/nGT/gU8z7ePxJ56hR9gQJk56jjmzpro7hDwVxbXl6tpXEB+fwIrlL/LYow/kOdKpKOVUP5csXU3dOob7RwxizpzF+a6fs2YvJCw8ggGDHmTWC5MpW7ZMkceSm5ziXLFiLSMfGsKI4QNZtGiFz7dDGa8l0549m/jKWs98/XgGBQXx8rxpjBo9gdTU1Ezr/K3vl1MfLbt2JD/9IYAud/ahW+gg9n54gCcef8htMWUnp37foUOfc/So5fChHdSqVYPNm7f57PHMqX4WtD/kbfXzjz/+xwsvLGD92ldp07o51apekr4uu36Av10/X5z7NJOnzCQ+3nnL7Ov9olWvv0lYeAR33NmHUSOHUrv2lQDUvvoK7ul6O0uXzOGGG+r7/HmbX/Wuu4aLq1zo6WL8Ky+9vJQN65cybGh/li9fk748OjqGMmVKc/jQDv735198tO8Tpj3zBKVKlyI11UHfPpmfLMnadh0+8gXhvYZyW5tu1KxRjfCwrm6LSeR8KIkvviQcWGWt/QdYA/TIY/sexpgdxpi9wIfA/dbavO5Q43GO2L8F51Q6a3LfvOAmTRxDdFQkbds0Z8Twgcx7KfM3wY0bNeCymtWpW685115bm9tvbwMOBy+8sIA77uzDiBGP0bXr7URHRfLgA4PP2X/16pdyxRW1AIiNjfPoaA+Anj3u5I7ObRkz5j7at2uZnqwNDg7OFL8DB9t37KZ12+4MHjKagIAAoqMiefWVc0eLBAcH06zZjSQmJhEbF0dgoGebtrRj2qpls2zXBwUFsWH9EkJCKtCyVVfi4xNwOBy8vno9HTqFc+/gUekj0bKblqZNm+ZUvvAC1q1ZzKWXXsT0Z8cVdUjn5frr67B1SySTJj/P8zPnA9BvwAP0DBvC7DmLcv13udZcTZUqlUlOTiYxMcnjx/DfcjgcnDr1B63bdqdf//t5773tbFi/lOioSC64oNI52999VyeGD+tPn96h9OkdSr1612azV/fIqy2qUKE827et46uvvuHue/o7Hx/04bYInDHv37eFQQN7MWDgg8xfsIwJ40fjcMDnnx+jddvuDBz4EPsPfEp0VCSb3lmZ7X6aNbsRSIvLO87dor62/PzTr8TGxRHaYzCxsXF0z2MaF0/473+7MWvWFDreHs6OnR/ku342btSAUqVKEhsbT0CA58/X7AQFBTH43t489fQsHh47iWnTnvTpdggyX0umzzg7LUnWeubLxzNtSoZal9fklUUzadCgLs9muJb7S99v0sQxxOzckGMfLbt2JD/9oSpVKlO3riEpKYnY2HiPtb959fs6dWrNBZUrUa9+C3bF7GVoRF+fPJ6Qc/3MytfrZ3RUJJ8c+oz2HXuy/8CnHP/6u9x/yY+un1WrXkKjRg14+qnHaN+uJWPG3EeVKpV9ul9Ur961hFSoQELCGVJTU9Pr0E8//cqsWQuZ8dxLjB49zKfP2+Ji0sQxHNgfxSNjR9D4/9ox9pEpPPnE2akA+/XtwfGvvqX+9S2JPR1LaGgXHA4Hjz46la7dBjB+wnSGDe1HdFQkPXvedc7+r7i8JtWqXwpAfHyCV5y/IhkFeGIOH5H8MsbUAL4CLOAAygJ/Wmv/z7V+BzDcWnvE9bkfUM9aO9oYUxY4ALS11v6YYZ8TgFNZXmy7A7gIaA60AhoDLwNLzuPFto4SJc/74YBzXH99Hd7f9RYhla4GnHOEX3ZZNTa+HcWihc+TeCYRh8NB124DaNTweqZNe5LExCQWL17J/AXL0vczamQEYWFdqXNdbfbs2U+/AQ+w4OUZlC9fjtTUVHqGR/Dzz7/+63KmJJ2gIHGm6dM7lB6hXShduhQdOoVz3XW1M8Vfq1YNXl81H0dqKp8dPca9g8/Op9im9a08+sj9tGjRlH37DjL16Vm0uPVmWrRsSmBgICNHjmNXTMFmQSqMOJctnUPTm2+kbNky7P7gIxYtWkHnzu14f/eHvLZ0Lu+//yEOh4OFi1dwzB5n4cLnSU5OZsuWHYwb/2z6fnr1uodhEf1o0qQhMTF7GTN2Mvv2fwI455lt3bYb3333Y07FKPI407z79gquv74O1n4FwNboGEJDu5CYmMTIkeP4YM/+9G2z/ttMnPQcryx+gZTkZL786hv69X+gUOeYK2icWY9Bo0YNqFu/OfcPH0THTq2pVvUSDh48TMfbe7Fi+YvUrFmd2NNxhPWK4LfffgecyYbVq+bToEFdTv72OytXrmXKVOdUUWnzUE+a/LxH44Sc26Lk5BSefOKh9Gkfpkx9gVKlSnqkLSrM8zYoKIjXls3liisuo0KF8jw5bhrbtr3P+rWvUKpUKU7+doqw8KGcOeMcpVOv3rXMmjmZJk0acuzLr5k3bwnly5ejd+/uBATAU0/PZs2avOcEPh9FeTwLem3pP/BBJox/mGtqX0npMqUJ7zWUY8eO51SMIo0zp/r52eFdfP/DCX795STf//AT9w4exaqVL513/fzyq28YO2Y4qakOli5dzZy5i/91GYsyzgcfGMKttzahTJnSvPTSEhYsXJ6vOL2tHcp6LbmgciXuvKsv93TtnKmevfnmJp8+nhlFRzmTorVq1fS6vl9hxDl92rhMfbQyZUrTuXM7Rtz/GK8sfiFTO1K+XNnz7g+NfWQKI0YM5OqrLicgMJABAx/k6NFjHoszp37flKkziVy9kJSUFMqVL0dExMNccslFPnk8c6qfBe0PeWP9fObpx2nbtiUJ8Qn06j2MFs2bctll1Xhzw+Zz+gE//viTX10/f/jBOevs4kUz2blzDxve2uzRflFB42zVshkzZkwgJSWFHdt307hxA/oNeIBGjRow9uH7CKkYwty5i1m4aIXHryvJp4r8dXzp3toczetrN3LoqKVRg3qMHj6I+nVMkf/d4CpXFrh+Lnl1NsZcRXBwMLNmLySAAC67rBpr173DkldnE3s6ltKlS9MzfAitWt7CqFERJCYmMfWpF9iwYXP6fqZPG5ep7eo34AFWrXgZh8PB33+fJrTnYOLi4v9VGVOSTgDo251c1Khcz+cT0j/+ccStx1hJfPEJxpiHgUuttaNcnwOAL4H21trjuSXxXZ/DgK7W2u4Z9jmB7JP4H+H8suADa20PY8wS3JDE9xWFedPqzRSnf1Gc/kVx+hfF6V8Up39RnP5FcfoXxelf3J3E95TCSOL7AiXx86Ykfv7p2RDxFWFA+lwA1loHsBTnlDd5stauAqoZY9oZYxq5kv79gAdcU+5UzrBtPM6X2Rb5VDoiIiIiIiIiIiIiuSnh6QKInA9rbcNslk3O8HPLLOuWZLN9xokqW2ZdD0zIsG33DD/3y0dRRURERERERERERAqNkvgiIiIiIiIiIiIi4haa3j3/NJ2OiIiIiIiIiIiIiIiXUhJfRERERERERERERMRLaTodEREREREREREREXGLVE2nk28aiS8iIiIiIiIiIiIi4qWUxBcRERERERERERER8VJK4ouIiIiIiIiIiIiIeCnNiS8iIiIiIiIiIiIibuFAc+Lnl0bii4iIiIiIiIiIiIh4KSXxRURERERERERERES8lJL4IiIiIiIiIiIiIiJeSnPii4iIiIiIiIiIiIhbOByaEz+/NBJfRERERERERERERMRLKYkvIiIiIiIiIiIiIuKllMQXEREREREREREREfFSmhNfRERERERERERERNwiFc2Jn18aiS8iIiIiIiIiIiIi4qWUxBcRERERERERERER8VIBDoceXxApJKpMIiIiIiIiIiIS4OkCeLOLKhqfz6H99pd16zHWnPgiIiIiIiIiIiIi4hYaVJ5/SuKLFKISJat7ughFLiXphOL0I4rTvyhO/6I4/Yvi9C+K078oTv+iOP2L4vQvKUknSD71taeLUeSCq1zp6SKIH9Kc+CIiIiIiIiIiIiIiXkpJfBERERERERERERERL6XpdERERERERERERETELVI1J36+aSS+iIiIiIiIiIiIiIiXUhJfRERERERERERERMRLaTodEREREREREREREXELh6bTyTeNxBcRERERERERERER8VJK4ouIiIiIiIiIiIiIeCkl8UVEREREREREREREvJTmxBcRERERERERERERt0hFc+Lnl0bii4iIiIiIiIiIiIh4KSXxRURERERERERERES8lJL4IiIiIiIiIiIiIiJeSnPii4iIiIiIiIiIiIhbOByaEz+/NBJfRERERERERERERMRLKYkvIiIiIiIiIiIiIuKllMQXEREREREREREREfFSmhNfRERERERERERERNwiVXPi55tG4ouIiIiIiIiIiIiIeCmNxBefYowJA5YBVYEAINK16j/Al0AcsBJIAiYDx13rywGLrbUvG2PKAkuAS4DSwGRr7dvGmAlAT2vttRn+Xj3gMNDKWrujSIMTERERERERERERyUIj8cXXhONMzHez1v5mrW1prW0JfAL0d31e4Np2dYb1twD3G2MuB+4A9ltrWwChwPMZ9h9sjLkhw+eewNdFGpHLuCdH8tWxvURHRbJu7Svpyx+4/14+PhDFwY+30r9fz1z3UbZsGWbPmkrc6a9p364lANFRkezft4XoqEjGjxtVlCGcl+dmTGTfR+9x+NAOmt96U/rysmXLsOmdlcTs3MDePe9y+eU1c93PDf+px+6Yjfz4/UEAatWqQezfx4mOiiQ6KpKbmjQq0jjy0rJFUz4+EMWHezfx0rxp6csXzJ/Bh3s38f6ut7juutq57uOiiy5k2dI5pCSdwJirAPjq2F52x2wkOiqSIYP7FGkM5yM0tAtff/URK5bPy7Q8v8fjyitr8fZbr5GSdIJSpUoBkJJ0Iv3377yzQ5HFcL7Cwu7mwP4oDh/akSmekJAKbHpnJXv3vMsH72/k4our5LofX4gVcj6HszNi+EB+PnGIp6Y+CuTcnnmLtNg+PhDFM08/nr7c39rbrLI793KStW736R3Kj98fTD9PS5cu7Y4i58vtndoQH/sNLZrfnL7s0ksvZnfMRnbteJPt0WupWDEk1320vu1WDn26nd0xGwFo0fxmTp08mh53Xtcmd8qp/c1OducrOK+lf/5xjD69Q4uwpPmXU2wF7SsAPP+csx/ywfsbqVz5giIp//lo0/pW9u/bwqefbMvUDq1b+wrRUZEc2B/FB+9vzHUfvtIOVahQnsOHdrB40cz0ZTn1B7PjC3GOHzeK6KhItkev5Uz8d5QrVzZ9XXZtU3ay6/fl1PfwpJzq54UXXsCObevYuX09Gzcso0yZ3K8T3t6Xz6nPXdA4wXvaoawK0verUaMau3a8yScHo5k8aaw7invecur/tL7N2Q4fObwzz/53YGAgkyaO4X+/Wwbf2xuAxYtmcuTwTqKjIpn3Yu7/Xu7y7DNPsu+j9zj48VY6dWydvtyf+0N5+f7Hn4gY9ST1mnUkMTHJ08URKRAl8cVnGGMqA/8HjALC8vO71tpEnCPqr7TWrrbWPutaVRP4McOm7+L8oiBNB2CKeZ0TAAAgAElEQVTvvy50PsUnJFCyZEk+/fSz9GV9+oQyfsJ0np85n/CwrlSsGMJbby5jy+bVvLl+CSVLlkzfNiSkAsuWvcHPP5/MtN+42DjKlCnNJxn26wnBwcEcOfI5TZt1JibmQzp2vC193TXXXEnDhtfTvOVdxJ6O45ZbmtChfSt2bFvH7piNRAzpm2lf5cuXY9r0uZmWJSenkJycQnx8At98+71bYspJly7t2b5tN91DB9Gnd3eCgoKoXPkCBvQPo/Md/+XNDZsY+VAEtWrVYMvm1WzdEskri1/ItI/KlSsxecrMc/Z9+nQspUqV4vDho+4KJ0exp+N4bXlktuuyHo+GN9QnOiqSndvXM2XyI5m2vaBSRaY+NeucfcTFJRAQEMAXX3xZJOU/X4GBgcyZNZXBg0fRt+8I/vfnn+nrKlUK4eUFy7jp5k6EVAyh4Q31fTrWNFnP4VkvTCHqvTfYuX09tWtfmWnbb779nnc3RWdall175i1Klgzm3ntHMWTIwwwa2Ct9uT+1t9nJeu6NHTOc7dFr2R2zkZtvapxp2+zqdkLCGUqUKMEX9jiJiYluKfP5qlGjGv379+TDDz/OtLxJk4aULFWS5i3vonr1qlxf/zr69A5lx7Z17Nn99jk37YGBAbz44quZlp05k0hAQAC/njzFyZOnijyW85X1GOX3fK1QoTyTJo5h0+Ztbi33+cjp2lLQvkLDG+rTK/wemjbrzPgJ0wkMDCjyWHLicEDffiMYMuRhev+3e/ryrvcMoHXb7pz89Teeema2X7RDs2dNZdO7Z68R2fUHfT3OiZOeo3Xb7myJ2sncua8QFxcPnNs25affl1vfw5Nyqp/Nm99M5QsvoEWruwG4557OPt2Xh+z73AWN05vaoawK0vcbPWooBz85QstWXdm9+yN3Fz1XOfW9Z8wYz1NPz+KebgP4/rsfCQ4OZsXyeWzZvJqo996gSpXK6duWLFmSXbv28PHHhzLtIy4untKlS/PJJ0eKPI7zMXBgOAMHPcS6de9w992d0pf7c38oL3+fjmVIv3ylj8RNHH7wP3dTEl98SXfgbWAzUNsYU/18f9EYcwnOLwCOZFj2Ac6pdx7MsOkmoLMxJsAY0xj4AkguhLLnadXrbxIWHsEdd/Zh1Mih6R2l+fOX8dKL03jmqcdZ/OpKwsPu5sYb/0NQUCBXX30FTW8+m3j55ZeT7D/waab9Pv7EM/QIG8LESc8xZ9ZUd4SSo+TkZF5d8jrzX55Op46tWbFyXfq6Q4c+5+hRy+FDO6hVqwabN29j/PjRVKwUQkpKCv37Zx4VG/P+h/z159/pn3/99RQ9eg6mQ6cw/j4dy4jhA90WV3aWL19DWNjdREet4ZVXX+eff/7hjz/+xwsvLGD92ldp07o51apewrCIftSufSUBAdD05sZcffUV6fuw9jhffpn5QZB7B4+iR9gQlr0WybRnnnR3WOd4d1M0//zzzznLszseY8YMp1q1qqSkpBDavUumkUsHPj7ETz//kmkfXe7sQ7fQQez98ABPPP5QkceSm4suupBKlSoSEdGXefOmUfvqszcy339/gq1bd3FgfxR//fk3Me9/6NOxpsl4Du/Zc4DB9/6XwMAAypUvR9cMNwUAb78dlelzTu2Zt9gStZPf//gfCxbMYNqzZ2+s/am9zU7Gc69EiSAmTxpLQEAAQSWCCAu7O9O2Wet29LYYwnsNpU27UDp2uI02rW91a9lzExQUxMvzpjFq9ARSU1MzrYuOjqFMmdIcPrSD//35Fx/t+4RpzzxBqdKlSE110LdP5hHoUVt3kZBwJv3z4SNfEN5rKLe16UbNGtUID+vqlpjOR9ZjlN/z9cW5TzN5ykzi4xPcVubzldO1paB9hatrX0F8fAIrlr/IY48+kOdIxKIUvS0GY67mjdULmDV7YaZ1vXrdQ2pqKm+/HeXz7dCQwX04dOgoRz6z6cuy6w/6epwAV111OYPv7c3Eyc8B2bdN+en35db38KSc6md0dAzffP097216nWrVq1Kt6iU+3ZfPqc9d0Di9qR3KqiB9v9pXX8F119Zmw/ol3H57W3cWO0/Z9b3BWeZ7ut7O0iVzuOGG+rRt05xOHVsTFBTIRRdfSPv2rdK3PXPmDFujYzL9/qzZCwkLj2DAoAeZ9cJkypYtU+Sx5OWll5eyYf1Shg3tz/Lla9KX+3N/KC/1rruGi6tc6OliiBQKJfHFl4QDq6y1/wBrgB55bN/DGLPDGLMX+BC431qbPoTHWtsU6AIsN8akDYGIxzli/xacU+mswU3q1buWkAoVSEg4Q2pqavqojCmTx3LnXX0J7zWUyRPH4nDA558fo3Xb7gwc+BD7D3xKdFQkm95Zme1+mzW7EYDY2DgCAz1X5SdNHMP+fVsYNLAXAwY+yPwFy5gwfnT6+k6dWnNB5UrUq9+CXTF7GRrRFxwOXnhhAXfc2YcRIx6ja9fbiY6K5MEHBp+z/+rVL+WKK2oBabF6dlTLo4/ez4zn5lG3fgv69+vBZZc5v3P6+JPDtO/Yk/0HPuX419/hwMH2Hbtp3bY7g4eMJiAggOioSF595dzRIsHBwTRrdiOJiUnExnn+eEZHRdKqZbNs12d3PBwOB6+vXk+HTuHcO3hU+mj1jNMIpKlSpTJ16xqSkpKIjY33eKxr3lhEQkICjz/xNGvWbOS+Yf3T1xtzFbfddguNGrfl9OlYBvQP89lYM8p4Djdt2pjg4GBuv6M3/+09jBUr1zH/5elER0VmOy1UTu2Zt7j++jps3RLJpMnPM33G2ekA/KW9zY9+Ax6gZ9gQZs9ZlGu9vtZcTZUqlUlOTiYxMclr4kt7tL3W5TV5ZdFMGjSoy7PPjktf369vD45/9S31r29J7OlYQkO74HA4ePTRqXTtNoDxE6YzbGg/oqMi6dnzrnP2f8XlNalW/VIA4uMTvCLunI5Tfs7XqlUvoVGjBjz91GO0b9eSMWPuyzTi0FPyurYUtK/w80+/EhsXR2iPwcTGxtG92x1FHVKOunRpz5EjX3BT09uZMvkRKlWqmL5u9KihzHtpCZC/4wre1w717HEnd3Ruy5gx99G+XUvatL6VihVDzukP+nqc4JxeJDLyLU6fjgWcI65rXlY9U9uUn37fH3/8mWPfwxPyqp+BgYEsX7mW9h17Ehcbx/Gvv/PJvvykiWOI2bkhxz53QeP0pnYoq4L0/X76+Veitu6kW+gghkb0pWrVSzwQQf789NOvzJq1kBnPvcTo0cNwOBycOvUHrdt2p1//+3nvve1sWL+U6KhILrig0jm/37hRA0qVKklsbDwBAZ7t606aOIYD+6N4ZOwIGv9fO8Y+MoUnnxiZvt4f+0MixVGAw+H+4f8i+WWMqQF8BVjAAZQF/rTW/p9r/Q5guLX2iOtzP6CetXa060W2B4C21tofjTGNgJPW2h9c2x4FWgLDgB3ARUBzoBXQGHgZWHIeL7Z1lCh53g8HnKNVy2bMmDGBlJQUdmzfTePGDeg34AGGRvSlbduWOBwO1qzZyPwFr7F+7SuUKlWKk7+dIix8KGfOOL8lr1fvWmbNnEyTJg059uXXzJu3hPLly9G7d3cCAuCpp2ezZk3u86vmJSXpBP82zqCgIF5bNpcrrriMChXK8+S4acTHxdO5czumTJ1J5OqFpKSkUK58OSIiHuaSSy5i2rQnSUxMYvHilcxfsCx9X6NGRhAW1pU619Vmz5799BvwAAtenkH58uVITU2lZ3gEP//8q0fiBOjW7Q4ee/R+4mLjiYuLp0yZ0rRpF8rkSWNo27YlCfEJ9Oo9DIDXV83HkZrKZ0ePce/gs/O6tml9K48+cj8tWjRl376DTH16Fi1uvZkWLZsSGBjIyJHj2BVTsNmeChrnsqVzaHrzjZQtW4bdH3zEokUr6Ny5HY88OoU1byzKdDyqXnoxCxc+T3JyMlu27GDc+GfT99Or1z0Mi+hHkyYNiYnZy9hHpjBixECuvupyAgIDGTDwQY4ePeaxOAGGRvSlT+/uhFQM4fEnniYhPoHOndsx7dm5rF41n9TUVMqULUN4r6GUL1fWI7EWRpxpMp7DJ376hVOn/qBJk4YknklkwKCHOHbsePq2Wzavpk6da0hKSuadd7eybt07mdqzsY9OKZQypSlonO++vYLrr6+DtV8BcEHlStx5V1+/am+zk/Xc27b9fbp2vZ3ExCRGjhzHB3v2p2+btW5PnPQcryx+gZTkZL786hv69X+AwupDFmac0VHOL2dq1arJZZdVY+26d1jy6mxiT8dSunRpeoYPoVXLWxg1KoLExCSmPvUCGzZsTv/96dPG0bFTa6pVvYSDBw/Tb8ADrFrxMg6Hg7//Pk1oz8Hp02R4Mk449xgNundUvs7XRYtXAM45fXfu3MOy194olHIVRpw5XVsK2lfoP/BBJox/mGtqX0npMqUJ7zU0U1vmzjjDw7vy8OhhpKY6OHToKJfVrEa/AQ/w11+n+ePUF1Sr0YCTJ09RsWKIX7RDfXqH0iO0C6VLl6JDp3CWLpmdqT+4bdv7Ph/nvo/e49npL7Jr1x5WLp9Hh07hJCc7H+hNa5u+/e6HfPX7ataolqnv8eabmzweZ071c/TDE3nrzaWEVKzADz+cILzXMNq0vtUjffnCiHP6tHGZ+txlypQulDi9qR3KqiB9v7kvLnYevwrl+fTTzxgw8MFc/lL+FDTOrP2fRo0aULd+cxo1asDYh+8jpGIIc+cuZuGiFaxa+RI1a1Yn9nQcYb0i+O233wHnoJvVq+bToEFdTv72OytXruXLr75h7JjhpKY6WLp0NXPmLvZonABLXp2NMVcRHBzMrNkLCSDAK/tDyafc8tpBAN7aHM3razdy6KilUYN6jB4+iPp1TJH/3eAqVwJ410gmL1OmTC2fT0gnJHzn1mOsJL74BGPMw8Cl1tpRrs8BwJdAe2vt8dyS+K7PYUBXa213Y8xDQC1r7YOuaXb2AZcD43Am8T/C+WXBB9baHsaYJbghie8rCruz6K0Up39RnP5FcfoXxelfFKd/UZz+RXH6F8XpX4pTnO5M4nuKkvh5UxI///QMjPiKMCD9zSrWWgewFOeUN3my1q4Cqhlj2uEcWX+xMSYGeAe4z1qbmmHbeJwvs3XbVDoiIiIiIiIiIiIi2dFIfJHCo5H4fkRx+hfF6V8Up39RnP5FcfoXxelfFKd/UZz+pTjFqZH4AhqJ/2+UcOcfExEREREREREREZHiS4PK80/T6YiIiIiIiIiIiIiIeCkl8UVEREREREREREREvJSS+CIiIiIiIiIiIiIiXkpz4ouIiIiIiIiIiIiIWzjQnPj5pZH4IiIiIiIiIiIiIiJeSkl8EREREREREREREREvpSS+iIiIiIiIiIiIiIiX0pz4IiIiIiIiIiIiIuIWDofmxM8vjcQXEREREREREREREfFSSuKLiIiIiIiIiIiIiHgpJfFFRERERERERERERLyU5sQXEREREREREREREbfQnPj5p5H4IiIiIiIiIiIiIiJeSkl8EREREREREREREREvpSS+iIiIiIiIiIiIiIiX0pz4IiIiIiIiIiIiIuIWxWVGfGPMTOAmnCE/YK3dl2FdG+Ap4B/gXWvt5Nz2pZH4IiIiIiIiIiIiIiKFxBjTAqhtrb0ZGAjMzrLJbOAeoBnQzhhTJ7f9BehtwCKFRpVJREREREREREQCPF0Ab1aiZHWfz6GlJJ3I9RgbYyYB31trF7k+fwH8n7X2b2PMlcAya+0trnWPArHW2jk57U/T6YgUHjXQIiIiIiIiIiIiucgrAe4nLgUOZPj8m2vZ367//y3DupPAVbntTNPpiIiIiIiIiIiIiIgUndy+uMjzSw0l8UVERERERERERERECs9POEfcp6kG/JzDuuquZTlSEl9EREREREREREREpPBsAboBGGMaAj9Za08DWGu/BUKMMZcbY0oAnV3b50gvthURERERERERERERKUTGmGeA5kAqcB9wA/CXtXa9MaY5MM216Vpr7Yzc9qUkvoiIiIiIiIiIiIiIl9J0OiIiIiIiIiIiIiIiXkpJfBERERERERERERERL6UkvoiIiIiIiIiIiIiIlyrh6QKIiBR3rjeRY61N8XRZREREfIWunyLexxgTANQBqroW/WStPerBIomIiPgFvdhWxMcYY2oCVwKHrbV/eLo8RcH1hu4cWWt3uassRcUYcznwDNAM51vK056M2g48aq094aGiFSpjzIPW2hcyfG5irf3Q9fNca+1wz5Wu8BljgoA7AQP8AxwF3rHW+vzF1hgTDAwA2pDhxhzYDCy11v7jqbIVBWNMT2vt6xk+17LWfuf6+Qlr7RTPla7wGGOutdZ+kcO6ztbat91dpqJgjLkst/XW2u/dVZaiUJzqZzG6fvbJbb21dpm7ylKUikOfD4pPHTXGdASeB74FfgMCgOpANSDCWrvDY4UrRMWlj5AdY8y1QDjQ3Vp7nafLU5iMMX2BUcCFOK8tvwAz09pbY0xta+2XHixigRSj9rZYxCnFk0bii/gQY8wQYARwGGhgjBltrX3Xw8UqCjuA48BeIBnnDUAaB+APF95XgaeAsLQEr2tEYRdgCdDWc0UrVF2AFzJ8fhq4zfVzHfcXp+gYY2rgvBnfC3yK87ztCkwyxnS31h73ZPkKwWs46+VzwEnO3pjfg/N8zjXh5IMGA69n+PwqZ8/d2wB/uUGfx9m4MMast9be7fo4EvCLJD7OhNJx4AfX56zXlduy/oKPKU71s7hcPxcC3wNROBNJAblv7rN24P99Pig+dXQccKu19lTGhcaYakAkzi/f/EFx6SMAzi8pgJ44k/e1cbbB7T1aqEJmjLkXZ0y3W2t/cC2rBTxnjKlorZ0DLAJaeLCYBbWD4tHe7qB4xCnFkJL4Ir6lH9DQWptkjKkMvAH4YxL/epwdxfbA58AaYLO1NsmjpSpcJay1URkXuKYDWGeMechDZSoKWZMO/pqEAHgRGGyt/SDjQmNMU5xfZNzhkVIVnqrW2p5Zlh0HdhljdnqiQEUst3PXn87jrLFUymWdL+sChALXAFuANdbaQ54tUqEqTvWzuFw/LwG6Ad2B64D1wFp/edIgg+LQ54PiU0cDgf9ls/wk/vU+vmLRRzDG3I+zflYHVgP9gcV++qTBYODmjNOzWWu/M8aEAR8ZY6rjTAj7suLS3haXOKUYUhJfxLecSbv4WGv/cE3d4XestUeAJ4AnjDGNcF6EpxhjjuBMvKzzaAELx3fGmDk4b8p/cy27FGeSyWcf08xG1mlkfH5amVxckjWBD2Ct/cAYc5EnClTIUo0xXYGN1tpkAGNMKZxJpkSPlqxo5Hbu+tN5nFssfhOna1qgt40xpYHbgSeNMbVxfhG+xlr7sUcLWHA51c978L/6mdP1szt+dP201v6Jc9TnImPMxTjje8311MEGa+1zHi1gISkmfT4oPnV0DbDXGLOJs/WzKtAJ59Ml/qK49BEmAj8Do4G3rLWJxhh/ii+jpOzer2KtTTbG/AP8Cdzl/mIVnuLS3haXOKV4UhJfxLcUp4QoANbaA8aYMkApnB2nIMAfLrz9cD6S2hdn8gHgBM7H5ld7qExFoYoxplOGzxe6PgfgnG/Sn5TMZV1pt5Wi6PQGJgEzjDHlXMtOA1vxn2kAMipvjLmOsyPqyrk+BwLlcv41n+fX1xVr7RlgrTHmI5zt74NAa6CJRwtWcFnrpwOIxT/rZz/OvX7+hPMJC3+6fmYUB/yFs829HLjYo6UpIn7c54PMdbQszmuL311DrbXTjTGRQCvO1k8LvJQ2RYmfKFdM+giX4vziOxyYa4x5DwgxxgT4w/uesjLG1LDW/phl2ZVAaWvtMx4qVpHw8/Y2XXGJU4oPvdhWxIcYY5I5+4hqAFAR56iAAMBhrfWbmzpjTAOgF86O42Gc82i+a61N8GjBCpExprQroYQxpg5QF/jCWnvYsyUrPMaYV3Nbb63t766yFDVjzEs4v4h5ylqb6lpWApgAlLHWjvJg8YqEMeYia+1veW/pe4wx23Nbb61t5a6yFKU8rish1tpSnipbYTPGXAj0wDkiKxjniNG11tpvPVmuwmSMCUxrfzIsOycp4etcL1a8ENib8YWgfvYy5mCco5fDcb5D5h0g0lp7wKMFKwLFoc+XG3+ro8aYZjjnTf8447RlxphB1tpFnitZ4SkufYSMjDEhON/1FI5ziq9V1toxni1V4THGtAbmALOBgziTvTcC9+N8KXNULr/uM4pLe1tc4pTiR0l8ET9hjCllrfWLx3GNMZ/jfFLoXZwvCo0nw+hQf3ijvDHmCeA6a20v13yT/YHdwH+ALdbaSR4tYCExxjS21u73dDncwTXK43mgA/AFznP4GmAj8FDa4/O+yhjTEbjTWhthjGmF8yVup4HywHBr7TseLWAhM8bU9LNRg8Waa2qHy3GO1l4LfEfm68r3nilZ4TDG3I3z3RtlcSZ7h1trY13rtllrff3FvemMMVOANsDvQC2cL7g97FrnN7EaY/4HnMLZF0qb7injObvME+UqbMWhzwdgjNlsre2Q4fN4a+1E18/+dN5OxPny2v1AO5zt0mGcL1E/bq39rweLJ4XEGHMJ0MNaO9vTZSlMxpjLgAjgWpxPVXwBLLLWfuXRghWSYtTeFos4pXjSdDoiPsw1J35bnCMiWgKXebRAhef1DD//X5Z1/vJG+S7W2rTYugNNrbUJxphA4H2cj1z7g2cBv7gxzYtrZMdQY0x54Cqc5+rXaYk0PzAJ6Oz6eQJwm7X2a9fo5ndc//mTpRSDc9cYs8Za283T5XCDX1z/heD80jTtZi7A9fMAD5WrsDwC3IDzKYpBQJQxpoO19i/86CWLLrdZa28CMMbUB1YYY3pbaz/Fv2J9MJtl/hRfmuLQ5wPnVA4Ztcjwsz8d1/YZ6ucUnO+p+AYYaa3d49GSFTLXgIaHAAP8AxwFns/u/Ui+zBXnSJwDUzLG6VcJfEj/Qv8xT5ejCBWX9nY1Z/t5/hynFENK4ov4IGNMCyAMuBMoAwwHhni0UIXrgL88Dp+LAGNMA1fS4SucN3cJOBNMgR4tmfwrWeb+T1PDGAOAtfZd95ao0AXjHHkPzkThN66f/8C/EhDFTWVPF8BNXvTzp4L+sdb+4fp5gTHmV+A9Y0xn/O89B0HGmLLW2nhr7WFjzD3AOmPMUPwr1mB/mXokD8WhzwfnnpsBuazzZelTVVhrY40xX1prm3uyQEXB9ZLih3AmfD/BeTxvAKYZY16y1q70ZPkKS3GJE8AYs4/s62LatLVZk8E+J+3pn+wYYyq5syxFyVo7IesyY0wVa+0pDxRHpFApiS/iQ4wxzwPdcE4DsAoYB7xnrV3u0YIVvpGAv9/QDcSZaCmHcz7qQ8aYz4AKOOde9Be3GGNOZrPc797jgPOJipw4cD7S6cumAweNMVE4E/dvGmM+wDlafbFHS1Y0GrtefpqV39zMuVxtjHk2p5V+NN+tvz8V9L4x5m2gu7U2wVq7wRhzBojG/14i/jxwxBhT31obZ6390jXd1yLOHXHny8JxxuTvikOfLzv+lLjPKGtcKR4pRdF7BOdTQRmfttzpGtARDfhLcru4xAnOe+zibB1+0k9ynZ8zgR9wPtW2Aijhuu8e5gcDq6QYUxJfxLd0wjmn25vAW9bak8YYf7wJCHLNL57t6F5rbbyby1PoXC/6uskYczHOeZoDgF/96QWLLrv98eVeOfDrkb7W2hWuecXbkOGcBfpba3/yZNmKyGc4X4Dq7+Jwxio+zFo7xhjTEjiTYdl7xpg9OF/m6zestauNMW9lfDmd66WgHYwxtT1YtMJW3hhzHTn3hY66uTxFxe/7fC71jDFvuH4OyPA5AKjruWIVupziBMBaG+qZYhW65OymS7TWnna9MN5fFJc4sdZ+l9M617vMprixOEXCGDMsh1UBQHV3lqWIPYlzyuHLcH5JfKe19lPXuxw24vsDq6QYUxJfxIdYa681xjTEOTrrA2PMt0AVY0wla+2fHi1c4fo/nEmlrI8ap81dfKUnClWYjDElcXYwJllrT7qW1TXGTLTWjvds6eRf8veRvrim63gj63JjzBPWWp+/ucniTG43dH7kF2vtUk8Xwg38/qkga+2ObJb97bpp9Ruu6+djxphJaS8MN8bUBUL97PpZG3iR7JPbDvzneuP3fT6XrE/rzc3hZ1+XW5z+pKQxpqLrvSPpjDFVOPf9B76suMSZl9vwgyQ+zieftgI/Z7Mu2M1lKUqJrvcbfG+MOeGavhZr7a+upxRFfJaS+CI+xlr7MfCxMeZhnC/FCgc+N8bE+NHolr3FYPT2dJw3qBnnv/8SCDHGjM9tzkIfMzWnFcaYm/3tJWfFmL/c3GSU4xRBxpjq1toT7ixMETrg6QK4SXF6Kigrf6ufxeX6+Ym11l8S9bkpDn0+gH1ZPjsyPk3iR4pLnDOBLcaY8cBBIAi4Efj/9u48yraqPNv+VRwRsFfAxMgrisiNRmOHYiQ2mOQFUaMRAYFPE40GG+z5jLE3qLEL2AB+MbYoiBE0RmM0vFHEJoICioA+JKAYjQgmmLxKI019f6xdnH02VXXOgao9a691/cY4gz3XqjPGPQd7n7XrWXM98zXAK1oGW2FDmedQPAF4J/CCqrpq/MToab6++GmSw6rqbVW1B0CSHYCX0LXYkWaWRXxpRlXVPHAKcMpoVdrebRNpMz20qh40fqCqfpXkJcCpQC+KEFX1f8bHSe5H16Jkf+ACukcd+6L3K32HZHKvkSTb0q0wPAi4I7Bri1wrraoOmzyWZFe6ee5XVfecfippWYO4fqp3zmXDfvFzSW4GfAU4tKr+s02sFTeIeVbV8Um+T7eP1V/Szfm7wLOr6rSm4VbQUOYJkORey5y+5dSCrKKqOme04f1irZBeMu08q+iPgcdNHLsj3b6CL5t6GmkFWcSXZsjoDvJHgcdU1Wq/A/YAACAASURBVP+Mjj0QeBvwhy2zrbA+PQ6/lGsXO1hV141uyvRGkl3oCoJPBq6i22Rxjx62Kun1St8h/HIzKcmtgScCBwL3pVuBtm9VfblpsFWQZEe6z+hBdG083gjs1TTUyvpA6wCraWCfz6FcP3v9nh0zhO98VNXdFjue5EDgXXT/9s68ocwTYPQ06Q2eKE1y86r6VYNIq2Io86RrX7aUvuzNAbAdsFOSc6rqZ2PH79Aq0EqrqiuSfH30dME5VfWzsW4Gv0fXUkiaSRbxpdlyDPCOhQI+QFWdkeQoui/GT2mWbGW9lv70el3Kz5L8TlV9ZfxgkscAFzfKtOKSnAXcBjiervh5bpKzeljAH4Kh/HIDQJJPAg8F/onu0eOTgdP7VsBP8ny64v2dgY8BTwPe18M9Dv4YOLZ1iFU0pM/nIK6f9P89u+C19P8735Kq6qNJntk6x2rr2zxHT6wdBewMnAk8q6ouSbIP8FdAL55iG8o8AZZbiJOkF/3ikxxC91TFd4AHJXke8C3g7cCd6Elxeyjz1DBZxJdmyx2q6sTJg1V10ujipNnxQuCkJN+l+1KxDtgduAv9Wv16OvBY4H50ezf8gA0fs+6TXvf/7/NTBku4BXAl8HPgv6vq6iR9fO++jm6Ds8OAv6+qq3o6z14b2OdzKNfPdUm2YfGNbamqvt2cGaQk64CtW+dYbT2c5zF018/T6FrtfWi0YebWdE/w9cVQ5rkxn6cfNxufDtx/1ILu14Cv093oP7yqTmgbbUUNZZ4aIIv40mzZZplzvXkEjgH0Fq+qf0tyf7qe8LvSFbaPAk4e7XfQC1V1yKgX6t50j1C/g64wsQ/wuaq6rmnAFTSE/v9J9gReBISupcV5wBFV9bWmwVZBVe2VZHvgAOCtSe4MbJXkXlV1XuN4K+nXgcfQfT6PSvJ5ug1C5/r0bxGwW5LTFzm+cF158LQDrbShfD6Hcv0EHkzXX3y8iD8/Gs8DO7UItQp6/50PYPS9Z9Lt6P7t/fiU46yaocwT2KKqvjR6/eEkrwReVFWfbRlqFQxlnhuz6M3UGXT5QgukqvppkouBR/Vw8+mhzFMDZBFfmi3fTPIy4M0Lv6iOHu87HPjnpslWVq97i4+Zo+tVfAu6gkufevler6quAT4DfCbJLYAnAM8B3g3s2DLbSutz//8kT6QrEL6cbvXrHHB/4M1J3l1Vx7fMtxqq6lK64uBRSe5K9//2Y0mu6EPRF6CqrgI+AXxitAfAvnSF/R8m+WhVvbRpwJVzLt3nspcG+PkcwvXz6wP5LjSU73z7sf4mzILrgHdV1efbRFoVQ5nn5CKU/+hpYXso89yYvtwgnpzHFT0tbA9lnhogi/jSbHkRcCRwYZKi+wwH+BTd4+WaEaNNij9H93jft+l+2Xki8BdJ9quqC1rmW2kLq3pHj/8fDxyfpBer6xYMoP//y+hWsfxi7NiXRqvu/plu3r1VVT+g2+z1jUl+q3GcVVFV/xf4IPDBJL9OV4zpiyt79FlczGA+n0O7fqo3PgK8qqoeOWotczKwA/DwJM+rqs+1jbdihjLPWya5J+tvVtxifNyjJ/aGMk+SvJXFi/VzwN2nHGe17JzkLUuNe7RwYyjz1ABZxJdmyKgAekiSW9F9mZgHLhz/pT3J7lV1WquMK2TJ3uILkjy+qj41jTCr5GjgTyfbHCR5KN2mO49rkmqFJdkDeD9w6yQ/AQ6qqkryHLoe3H1pBwD97/9/9USBEOgKv0mubhFoNS2ymduzR4/kPho4gp5s5pbklnQ3iBfmedSozdU8XZ/xdzWMt5Let7EfmPHr55A+n4O4fgJD2etoo9/5euINwMGj108EbkW3EOf2wCfpbkz1wVDmeTldv/jFxvP0o386DGeeAOew9Pf270wzyCp61UbGfTGUeWqALOJLM2j0i/q3lzj9l8z4F6rJ3uJLeAHdEwiz6tcW61NcVV8b9eHui7cAj66qC5M8HPjAaGXWmXT9fntjAP3/b57ktlX13+MHk2wHbNUo02qa3Mztgz3dzO39dL3TT6BrpfPWJD8EDgXe3DLYSqqqj2zCj83y9XNIn8+hXD+vSXIy3Q2204EXVNXFjTOthnOT/A3r53n4YjekeuDKsadE9gE+MmqN+V9JrmmYa6UNYp7LtYAa3VDshaHMc+RQbtgKasE8cOx046yKLavqva1DTMFQ5qkBsogv9U9fNt7ZmFmf53L9e7eeWorVd3VVXQhQVaeOem4fXFVnN861Knre//8I4J+SvAY4C1gHPAh4DfCKlsFWyVA2c/uNqjoAYLSp7cXAh4AHVtX/NE02fbN8XRnS53Mo18+jWX8j8XF0/48PappodfwN8LfA2+ieZvsr4JCmiVbHVkm2oHuP7gO8aezcrdpEWhVDmecGktyPbt+V/YAL6Tbe7p2ez/NJrQNMwUHAEIrbQ5mnBsgivtQ/fWrfsZxZn+dpo6LgGxdWaI9Wcb+Wfm1SPLn6/NK+FvAX9Lj//6/TtV15Hl1veIDv0rWZmdUWJMsZymZu16+MrKr5JOcNuFfoLF9XhvT5HMr1c11VnTp6fWKSQ5umWT23qqqFFa6V5ItN06yeDwNn0D0Z87lRa8GtgPcApy77N2fLUOZJkl3oioVPBq4CtgX26Nv+K0OZZ9/ms4RbTexxsIEe7XEwlHlqgCziS1IbL6ZbVXdBku/R/Xu8C/Bp+rVJ8XajdjILth0f96koOoD+/9sDHwB+TLdC9MTJ1h09M5TN3CYL17NcyB6yIX0+h3L9nLyROOst2ZYyOa9e/htUVcck+QfgtguLGarqqiSn0n12e2Eo80xyFnAbusUa+1bVuUnO6lsheCjzHJB70H1HWKpl0Ky2FJw0lHlqgCziS/0zy+0ANsesz/NK4KvAFcAPgFPoHku9lm414Ssb5VppZ9A9crvgzLHxPNCbIj497/9fVS8HXp7kAcD+wL8kKbpf7P6+qq5qGnDlDWUzt4cluWT0eg647Wg8B8xXVR+eItlUM3tdGdjncyjXz52TvGWpcY+emNlhdLN70XFVHbPI35lJixU+q2qjm27PmoHM83S69k/3A76b5Af08wbUUOY5FN+qqr58f13OUOapAbKIL82QJI+tqs9s5MeOn0qYVZTkdlX18yXO7VZV36RbhTfL3k3X1/d04PF0jx2fT7eR5IkNc62oqnraxn4mySFV9dfTyLPKBtH/v6rOpLsx8bIkDwReTtd38rZNg62w5TZzW5Dk8VU1yxtsU1VbbuxnkuzY11V3Se4GPLmq/pIeXD8H8vkcxPUTOBL4r7Hxq8Ze92kD3+OA7Vh/E+144IHAJXQ3aqQ1p6oOGbXx2puu1cw7gHWjp00/t9Dqa9YNZZ5DN/FdqLeGMk/1m0V8aba8mG7TzCVV1d9MKctq+gRjq1yTvLuqnj0avgV4VFV9ukmylXOfqtoDIMn76DaT/Gdg76r6QctgDRwA9KGIP5j+/0l2o/v/9jjgbOCP2iZq5gXATBfxN9EH6M+TByS5E93798nAHeg28u3L9XMIn8+hXD8fN76ScPy7UJIv0G0E2wdfAV5ZVXuOnl47GdiBrqj//KbJpGVU1TV0v5d9JsktgCcAz6G70bhjy2wraSjzHIjrn45e6rtQTwxlnhogi/jSbFmXZBuW3qTl8innWS2T89t1mXOz6lcLL6rq6iRnV9X+LQM11Jf/p73u/5/k/nRfgp8IXAB8FHhdVf2iabC2+vLe3ZiZn2eSOwBPoltJuDNwEnC7qtqlabAVMrDP51Cun5Ofuyxzbpa9ATh49PqJwK3o5np74JPAPzbKJS0pyTOq6r0L49HvYMcDx4+uN70wlHkOyHVJ/pSefhcaM5R5aoAs4kuz5cHAuWz4y9v8aDzP7G+cuWC5Xot96cPoZpLr9WXufe//fzRd24M9qurS1mHWiL68dzemD/O8GPg34CXA56vqutGGfX0xpM/nUK6fk/Oa/O7XF1dW1QWj1/sAH6mqeeC/klzTMJe0nIPoWpXdQFX912LHZ9RQ5jkUff8utGAo89QAWcSXZsvXN6VPcw/16ZfVBbslOX30eg7IaLywmeTMb4Q6NH3v/19VD22dQboJ/gg4EHg/8OkkJzTOs6IG9vkc6vWzj9+FALZKsgWwNV0R/01j527VJpK0UbdMck+Wfjr6vCnnWS1DmedQ9Pq70JihzFMDZBFf0lq03C/ofXkM7j6tA6whfWoLsDF96f+vzlDeuzM/z6r6KPDRJLene0Lm1cCuSd4KfMBCxEwZyvVzCN+FAD5M9yTbVnQbZVaSrYD3AKc2TSYtbRe6J6AWuz7O0599ZIYyz0EYynehocxTw2QRX5otz1vqRJKbjTYe6oPe/4JeVRe1zjANow2wljTqrfnSKcVZC2a+GKoNHNE6wJR8oXWAlVJVl9EVB9+T5M50rQKOBXZrGkybbCjXTwbwXQigqo5J8g/AbRc2g6+qq5KcSreptrQWfWt84+keG8o8B2WR70IH0sPvQkOZp4Zlbn6+r09mSv2T5INV9cdj4+tbcyT5Qp++ZCVZBzyebnOza4HzgH8Y9UnVjEjyfdbv2/BrdD0KYX3bg77s47BJ+vY57bMkl7J4+4qF9+4dpxxpVST5OMu06ejThqFeVyRJKyHJF4fQ4nQo85SkWeFKfGm23GViPN6aozcrfJPsAHwO+Drwbbq5PRH4iyT7jW2ApjWuqu628NpfBDRLqmr7hdc9f+8eNfb67cALWwVZTRu5rjypqi5smU+SNFP2W+pEkt2q6pvTDLOKhjJPSZoJFvGl2TZeuO/TSsKjgT+tqq+NH0zyULoi0+OapNJN1af36I3Vm5ttA9Pb925VfWnhdZKfj497ZrnryjvwuiJJ2kRV9bNlTr+FnvSKH8o8JWlWbNE6gKSbpK+FpV+bLLQAjI5tv8jPS80l2XWZc48dvRxS/3/Nnr5eU8DriiRpOoayYGMo85SkNcOV+NJs2TnJWxYZzwF3b5RpNdx8mXNbTy2FbrIkzxkb7jAxpqqOmXKk1XQMYyuSknyyqv5wNHwx8Jmq+kaTZNpsSfYZG247MaaqPjvlSKtiYvPpdUm2YewX89Hm033gdUWSNA19viE+bijzlKQ1wyK+NFtetcz4nGkGWWWnJXkl8Maqug4gyc2A1wL/3DKYNtv4Ctfj6PeK18kVSbdb5pzWvvE+sGdOjOeBXhTxgXNZv/k0dJu9Lozngb5sPu11RZK0IpJ8g8WL2HPALlOOs2qGMk9JmhUW8aXZcnxVXb3YiSQPn3aYVfRi4AjggiTfo/u3ahfg0/R008UeO6OqPtM6xJQstyLJ1Uqz5+iBbNi2y1LXlZ7xuiJJWilPah1gSoYyT0maCRbxpdlycpI/rKrLFg4kmQNeA+wP3KtZspV1JfBV4ArgB8ApwIXAtXSrJl/ZKJc234uBoRTxJ1m4n21D2bDt8wxjnl5XJEkr5W5VdcrCIMlWVXXV6PUzgPe2CrbChjJPSZoJFvGl2XIk8IUkB1TV+Ul+Azge+DfgQW2jrah30/UvPh14PLAVcD7wZuDEhrm0+W7QY3tcj/ptAzwsySWj13PAbUfjOeA27WJJyxpKqyevK5KklfJqupvBC/6R9TfED6I/xe2hzFOSZoJFfGmGVNWnklwInJDkY8DTgZdX1UmNo620+1TVHgBJ3gdcTNezeO+q+kHLYNpsD6bruT1eKOxjv22qasvWGbSifmfspsy4OWC+qu447UCr5DeT/O1SJ6tq/2mGWUVeVyRJK2XyBvjcMudm2VDmKUkzwSK+NGOq6jtJfh84CXh7Dwv4AL9aeFFVVyc5u0eFpKH5elXt2TrEtCT5E+D9VTU/Gt8F+N2q+kDbZLoRvjqQ9+6PgaNbh5gCryuSpJUy2TJxfplzs2wo85SkmWARX5ohSb7B+i9MNweOSPI01q8MfXCzcCtruS+M0pqU5NXAfYETgF+ODv8S2CfJ1lX17mbhpKX9vKq+1DrEFHhdkSStlPF2kXNj4y2AdU2TrayhzFOSZoJFfGm2PKl1gCnZLcnpo9dzQEbjvt2sGIJPJNmmqq5oHWQKHgc8pKquXThQVf+Z5GDgC3Q9uTU7jm8dYEqGsvG01xVJ0krZka5d5LiF9pF9ukk8lHlK0kywiC/NkKq6aNSeYyfgnKr62cK5JL8HXNQs3Mq6T+sAWjF7Ai8b7eXwxdGfr1XVVW1jrYpfjRfwF1TVr5K4Wmn2PDvJK4FTGb13q+r7jTOthhcmuT/r53hh60CrxOuKJGlFVNVdk9wHeC5wL+Ba4CzgiKr6UdNwK2go85SkWTE3P+8NVGlWJDkEeD7wHeBBwPOAbwFvB+5UVQ9rGE9aUpJdgYcDD6N77/4E+EJVHd402ApK8gXguVX13YnjuwFvHUh/9V5Jchvgoax/794R+Brde/fDLbOtlCS3BX6Hbn4PB7YHvsr6ov4PG8aTJGnNSbIn8C7gDcAZwK2B3YAXAc+pqi80jLdihjJPSZoVFvGlGZLkNOBho5W9vwZ8HbgcOLyqTmibTlpekq2A36Yrij4WuH1V3bNtqpWT5EHAR4BP0t1cWwfsDuwDPLqq/rVhPN1ESe4KPAp4BnCfqrp120SrI8nWwL50q+52ryqfIpEkaUySk4FDJp9eS3J34LiqekibZCtrKPOUpFlhOx1ptlxeVb8CqKqfJrkYeNRA+o1rBiXZm25172/TFbVPp1vl+zdVdWnLbCutqr4xKuQfSFe8nwfOAf68qn657F/WmjP29MjDgdC1K/sK8ALgzIbRVlySXejm+QjgnsCP6G5GvaRlLkmS1qgtF2s/V1UXJLmuRaBVMpR5StJMsIgvzZbJR2eusICvNe6vgFsCHwZOBk7raT98AKrqf4C/bp1DK+I04DLgfcArqqove45sYHQz+Eq6eb6hqr7XOJIkSWvdcgXsK6eWYvUNZZ6SNBNspyPNkCQ/BMbb5jx5fFxVL516KGkjkmzL+p7bu9P9QvAvwJer6h9aZltJSS7lhjfaAOaA+aq645Qj6SZIsgVwP7r37cOAHYHzgS8DX6mqcxrGWzGjp0cW5rgDcB7dEwdftqAvSdINJfkfYLFr5BywS1XddsqRVsVQ5ilJs8IivjRDkvzRMqfnq+rYqYWRboQkvwHsBRwCPKCqbt44krRJkmxJd+P0ucCD+torPsk9gT2BpwI7efNJkqQNJdlxufN9eXpvKPOUpFlhEV/qgSSPAg6oqkNaZ5HGJbkb63tt7wH8AvgicApw6qj9TG8k2QO4B3BGVX1n7Pgzquq97ZJpc402Yn4I6/vi7wJ8g+69+8WqOrddupWV5OZ0T8ksfFbvAZxBN8+jW2aTJEmSJNkTX5pZSXYHDgL2pXvM8QNtE0mL+hRd0f7vgZdU1WWN86yaJK+ju1HxTeAFSY4EvgMcA1wAWMSfLT+ka/t0CnAYcHZV9W7lQ5JTgZ3pNp3+InBYVZ3dNpUkSZIkaZwr8aUZkuS36No5PBn4GfBR4E+q6t5Ng0nLSLIOeDwQ4Fq6ntv/0LeCaJKvV9VDRq9vBfwr8H26mxf/0jScNluSLarquiQPYOy925de+AtG8zurquaT3Am4tqouaZ1LkiRJkrSeRXxphiS5jm7V/Z9W1VdGx86sqge0TSYtLsmdgc8DXwe+TbcR1v1Gf/arqgsaxltRSb5YVXuOjU+tqoe3zKQbL8nt6J4kuZoN37vXAAdX1aUN462Y0Qa+rwGeDvwU2AK4Pd2TI39ZVdc1jCdJkiRJwnY60qzZAzgQ+FiS8+hW4vs51lp2DN1Np6+NH0zyUODtwOOapFodk3fFr2mSQivlncA7q+qk8YNJ9h2dO7BJqpX3CuC2QKrqcrj+SZLXj/68vGE2SZIkSRKuxJdm0mjl5O/R9cT/A+CfgQ9U1WebBpMmjLeY2ZxzsyjJJXT906Fbtf2IsTFVtf/0U+nGSnJ6VT14c8/NmiRnVNUDN/ecJEmSJGl6XMErzaBRe4N/Av4pyVZ0q5n/BLCIr7Xm5suc23pqKaZjv4nxUU1SaKVsscy5dVNLsfp+tcy5q6eWQpIkSZK0JIv40gxJslR/7UuAd0wzi7SJTkvySuCNC721k9wMeC3dEyS9UVVfSvK/gJ2A71TVf7XOpJvk/CQHV9Vx4weTPBM4u1Gm1XB5kntPbtg72vD2F40ySZIkSZLGWMSXZsspwAV0m4ReTdeyY8E8cGqDTNJyXgwcAVyY5Lt0151dgE8DL2oZbKUlOQR4HvAd4L5JDrPF1Ux7PvDhJIcC36Jbfb8b8B/AAS2DrbCXAicl+SRwFt08HwQ8BtinZTBJkiRJUsee+NIMSXJv4MnAXsB3gROBz1XVcu0QpOZGG2Xene5m04VV1bsVvkn+BXhEVf0qyR2Av62q32udSzdNkgC70r13v1dV5zeOtOJGn8+DGZsncFxV/bJpMEmSJEkSYBFfmllJHsj6gv45wIlV9Ym2qaQNJZmj24D5HsCZVfX3o+NbA6+sqle2zLeSknyxqvZcaqzZk2QP1r93zx47/oyqem+7ZCtrtLfKnYAfVdU1Y8fvWVXfbZdMkiRJkgS205FmVlWdkWQbYCvgCXQtECzia615N9179DTg2Ul2Af4VeDPdkyR9MnlX3LvkMyzJ64A9gG8CL0hyJF2rpGPo2pr1ooif5Al0e6r8BLhjkgPp5vlauk3T79kunSRJkiQJLOJLMyfJfenaHjyGrtDyceDPquqKpsGkxd2nqvYASPI+4GK6DW33rqoftAy2Ch6W5JLR6zngtqPxHDBfVXdsF003wl5V9RCAJK+nu/n0feDFVfUvTZOtrJcB96uqy0atg06ie88eC9y3aTJJkiRJEmARX5opYxuDfhY4DLicbrXvg5JQVW5sq7Xm+v0aqurqJGdX1f4tA62WqtpyYz+TZMequmgaeXSTXX9jtKp+keRfq+rhLQOtkiur6jKAqqokVwGPqaqLG+eSJEmSJI1YxJdmywljrx88cW4esIivtcYWMxv6APCo1iG0SSbfq9cs+lOz77qJ8X9bwJckSZKktcWNbaWeSfKaqnpd6xwSQJL/Ab43Gs4BGY0XWsxM3ozqNTe7nR2jVkinjB165Pi4L0+UJDkfePvYoReOj6vqmKmHkiRJkiRtwJX4Uv88onUAacx9WgdYY7xzPjv2mxgf3STF6jsO2H6ZsSRJkiSpMYv4Uv/MtQ4gLaiqi5Ksq6prAZI8ErgFXR/uLzQNJy2jqr40Pk6yM3Bz4KqquqBNqpXnk1uSJEmStPZt0TqApBXnSl+tGUkeAHw3ybrRoXfRrXB+a5KntkvWjDfZZkSSuyX5yth799N0q/H/T5JHN4y2opJsk+T1Y+NvJrkkyQ+TpGU2SZIkSVLHIr4kaTUdAfzxwkp84GdV9TTg0cCz2sVqxqcPZsfRwDvH3rsXj/Yz+F3gz9rFWnFHALdNsvCd8P9W1R2BPwHe2C6WJEmSJGmB7XSk/nGlr9aSLarqa2PjLwFU1SVJftUo06pJ8jTgRcC2dE/FXAwcWVXHAVTV4Q3jafPcpqr+dmxcAFV1YZI+/Tu7W1U9aPJgVZ2cxFY7kiRJkrQGuBJfmiFJXjoxftoiPzbEFiVau7YeH1TVa8eGt5lulNWV5FnAY4DHVNWdq2oHYF/gD5O8sG063Qhbjg+qavzJkVtOOctqmryZNt4qqE83KyRJkiRpZlnEl2bL3hPjp0z+QFX9+5SySJvizCSHjR9IsmWSNwGfa5RptTwTePL4Z7CqLgIOZJHPqta8C5LsO3kwybOBMxrkWS1XJNllYVBVVwIk2Q34ebNUkiRJkqTr2U5Hmi2TqyJdJam17sXAkUn+DTifbnXzPYC/G53rkyur6prJg1V1dZKrWgTSTfIC4COjpyi+Q/ed6UHARcBBLYOtsJcBn0pyIuvn+WC6FfmTN44lSZIkSQ1YxJdmy/xGxtKaUlWXA4ckuRVwd+A64ILRcQCS7F5Vp7XKuJKS7FBVP5o4tlOrPLrxqupSYK8kAXale+++uaouWPiZJDuOnraYWVX1zSQPprsx8dt08zwb+POquqJpOEmSJEkSAHPz89YApVmR5BLglNFwDnjE2Jiq2n/6qaSbJskXqupRrXPcVEl+H3gn8A7gLGAd3crt5wIHV9U3GsbTKujLe3djknyyqv6wdQ5JkiRJGipX4kuzZb+J8VFNUkgrqxdtoarq5CR7Ac8G9qJ7UuZ7wO+6V0Vv9eK9uwlu1zqAJEmSJA2ZRXxphlTVlwCSPAAIcC1wXlWd0zSYdNP04pGwJM+oqvcCf946i6amF+/dTTCUeUqSJEnSmmQRX5ohSW5PtyHo1cC36VaBPivJNXTtOi5tmU8auIOA97YOIUmSJEmS+sUivjRb3gG8s6pOGj+YZF+6XtwHNkkl3TR9aUlyyyT3ZIn5VNV5U86j1deX964kSZIkaQ2ziC/Nll2r6qmTB6vqpCR/1iKQtJwkj62qz2zkx46fSpjVtwtwNIsXdueB3m+A2idJdquqb27kx74wlTDtXdY6gCRJkiQNmUV8abZsscy5dVNLIW26FwPLFvGr6m+mlGW1fauqLNT3x1vYyI2Xqjp8SllWTZIXVtXbx8a7V9Vpo9dHVdWhVbVvu4SSJEmSJIv40mw5P8nBVXXc+MEkzwTObpRJWs66JNuwdIuZy6ecR9KG/gB4+9j4L1l/8+Je048jSZIkSZpkEV+aLc8HPpzkUOBbdKvvdwP+AzigZTBpCQ8GzmXDIv78aDwP7NQi1CrZb2M/kOQ1VfW6aYTRTfY7SS5Z5PgcMF9Vd5x2oFUyeYPNPv+SJEmStMZYxJdmSFX9DHh0kgC70hVB31ZV5y/8TJIdq+qiVhmlCV+vqj1bh5iG0edzYx6x6kG0Ur46kPfu/EbGkiRJkqTGLOJLM6iqCqglTn8AN9CU1ipXOWut2S7JPmPjbUfjOWDbRpkkSZIkSWMs4kv9Y5FQpldjPwAAFf1JREFUa8nzWgdYY1zlPDve0DrAlJzBhq2gzhwbnzn9OJIkSZKkSRbxpf6xSKi15I+SLPmerKqXTjOMtBl2SbLLUier6phphlktVfW01hkkSZIkScuziC9JWk3ntA6wxvikzOzYvnWAaUjycTa8+TsP/AT4x6r6fJtUkiRJkqRxFvGl/rFIqLXkJ1X1TwuDJPevqrNaBloty20qneQVVfUG4KlTjqUb75NVdfbCIMmdquonLQOtkqMWObY9cGiSXarqXdMOJEmSJEnakEV8qX++0DqANOZlwD+Njf+K/m68/NkkT6+q0xYOJPk14HjgYoCq+vdW4bTZ3s6G79Xj6OF7t6q+tNjxJJ8CvghYxJckSZKkxrZoHUDSpkuyRZJnjI3/LsnpSU5NcmeAqjq8XULpBiafDOnzkyKPBY5KcgBAkkcDXwU+UFUHN02mG2NI790bqKqrgatb55AkSZIkWcSXZs3hwGOSrBuNtwX2A44F3tAslbS0yU1te7vxclV9n26l9oFJ/hF4OfD7VfWRtsl0Iw3mvbuYJAcCl7bOIUmSJEmynY40a/YCdq+qa0fja0Y9uN+b5JkNc0lL2SHJc5YaV9UxDTKtiiS3AK4FnkJ3U+0K4OLRcarq8obxtPm2S7LP2Hjb8XFVfbZBphWX5FK6GxTjTxpcCXwZeM6if0mSJEmSNFUW8aXZcsVYAR/gaWOvbXugteg4uk0ylxr3ybmsX629UBDdf/TfeWCnqSfSTXEG3ZNOC84cG88DvSjiV1VfP4+SJEmS1Btz8/ODejpcmmlJvgLsW1U/nTi+M/C+qnpEm2TSxiW5E3BtVV3SOoukTpKnLne+qo6dVhZJkiRJ0uJciS/NlsOBf05yJPAdus/wg+laHhzQMpi0mCRzwOvonhr5KbBFktsDfwO8qaqua5lvpSW5B/A8IHStdc4D3jVqe6UZk+RpwIvo9h+ZBy4Gjqiq45sGW1mLbdh7M+BZwA50e65IkiRJkhpyJb40Y5LcFTgE2BW4jq6Fx/9XVf/RMpe0mCSvoiuAvnyhJ3ySWwGvBy6vqpe3zLeSkjwMOAZ4C/AtuuLo/YCXAIdV1ckN42kzJXkW8HvAi6rq30fHdgT+CvhqVR3ZMt9qSXIA8DLg74C3VdUvG0eSJEmSpMGziC/NoCTbAbsA1wDnV9XPG0eSFpXkjKp64Oaem0VJvgo8qap+MnH8TsCJVbVHm2S6MZKcQbeR+DUTx7cEvt6n9y5Akj3pNmQ+AzjctleSJEmStHbYTkeaIUm2Bt5D10LnHGAL4F5Jvgw8v6quaJlPWsSvljnXu82YJwv4C8dGbYU0W66cLOADVNXVSa5qEWg1JLk38CbgF8BTquqCxpEkSZIkSRMs4kuz5c3At6pqg40Ik7wIOJKuh7G0llye5N5Vdc74wSQPoCsa9snWSW5eVRvcuEiyDbBNo0y6CZLsUFU/mji2U6s8q+RbdHs3nAG8IsnC8Tlgvqqe3iqYJEmSJKljEV+aLXtU1QsmD1bVkUm+2SKQtBEvBU5K8kngLGAd8CDgMcA+LYOtgo8An0jykqoqgCS/BbwNeEfTZLoxXgucnOQdbPjefS5wcMNcK+3urQNIkiRJkpZnEV+aLdcuc+66qaWQNlFVnZHkgXRFz4cA88B3gVf2bcPM0c20nwAfHG1ADXAh8Paq+ni7ZLoxqurkJHvTPeG0F91793vA7y5sdNsTd6uqUxYGSbaqqqtGr58BvLdVMEmSJElSxyK+NFt+muSR4wUXgFGh6cdtIkkbdTXweeB94z3Gk9yzqr7bLtbKq6oTgBOSzFWVO8fPuKq6KMnLJ/9fJtmuqn7WKtcKezVwytj4H4FHjV4fhEV8SZIkSWrOIr40W15I15qk2LC9w13pVopKa0qSJ9C1kvkJcMckBwLfoWtV8jjgnu3SrbwkTwNeBGybZB64GDiiqo5vm0ybK8kewPuBW4+esDiwqs5P8hzgMKAvvfEnN12eW+acJEmSJKmBLVoHkLTpqupC4AHAB4Ar6TYGfTfwgKq6pGU2aQkvA+5XVQ8BHg28D/gG8J/AfVsGW2lJnkXX6/8xVXXnqtoB2Bd4YpIXtk2nG+EtwKOr6jfobsx8MMlpwH2ABzdNtrImnxiZX+acJEmSJKkBV+JLM2bU1uHzoz/XS/LKqnp9m1TSkq6sqssAqqqSXEVX5L64ca7V8Exg9/GWQaN2LAcCXwfe3iyZboyrRzdOqapTk9waOLiqzm6ca6WtS7IN61fdL4y3oHvaS5IkSZLUmCvxpf541MZ/RJq6yQ2X/7unBXzoblhcM3mwqq4GrmqQRzfN5Hv30h4W8AF2BM4d+3OX0X/PAf5Xw1ySJEmSpBFX4kuSVtMOox7ii46r6pgGmVZNkh2q6kcTx/rSO31otkuyz9h42/FxVX22QabVsFuPNumVJEmSpF6yiC/NkCT3Wub0LacWRNp0xwHbLzPuk9cCJyd5BxtuPP1c4OCGuXTjnAHsNzY+c2w8D/SliP/NJN+l+2x+sqp+2TqQJEmSJGlDc/Pz7lkmzYokX1zufFXtOa0s0uZIMjfaz6HXktwFeDawK107liuBP5tcnS+tJUl+B9gf2Ifu5sXxwGdHraAkSZIkSY1ZxJd6Jsnjq+pTrXNIAEkeCrwPuDXw78AfVdX5bVOtjtFcXw38CPgr4GPAFcCvA8+tqs80jKfNlGRX4F3AzsDpwAt6vJ8D0N1sAx4GPAn4HeCbVfWnbVNJkiRJktzYVuqfF7QOII15M/CYqtqB7r35lsZ5VtNbgTcAnxv9eVJV7Q7cH3h5y2C6UY4GDqd7quLjwBFt46y+0dMy/wlcRvcUyXZtE0mSJEmSwJ74Uh/NtQ4gjbm2qi4EqKrTk9yudaBVdFVVfRkgyUsWnjioqv9KclXbaLoR1lXVqaPXJyY5tGmaVZQkwJPpVuBfQtdOZ5+q+nnTYJIkSZIkwCK+1Ef2yNJact1Gxn11xcTYz+XsGcR7N8m3gauAjwJ7VdV/NI4kSZIkSZpgEV+StJruneRvR6/nJsZU1f5tYq2K3ZKcTjfPjF4zGu/SLpZupJ2TvGWpcVW9tEGm1bDfUvtUJNnBTZklSZIkqT2L+FL/2E5Ha8l+E+OjmqSYjvu0DqAV9aqNjPvincDeC4Mkr6mq142GxwKPapJKkiRJknQ9i/hSTyR5bFV9hgFsvqjZUVVf2tjPJPlkVf3hNPKspqq6qHUGrZyq+tDGfibJu6vq2dPIs4q2mhg/Yuy1N4UlSZIkaQ3YonUASSvmxQBV9enWQaTN1OfNbtVvaR1gBUzu1zC3zDlJkiRJUgMW8aX+cMWkZpWFQmnt8PMoSZIkSWuM7XSk/rDwIknaXBtsNj02ngN+s1EmSZIkSdIYi/jSDElyKYsX6+eA20w5jiRp9k1uPn302Os+b0QtSZIkSTPDIr40Q6pq+6XOJZncnFCaFZe1DiDdSDPfxqyqvjS6ftwJ+FFVXbNwLsk92yWTJEmSJC2wJ740w5KsS7J3kmOBf22dR9ocSU4EqKp9W2eRJi28Pzfif696kFWW5AnA+cAJwPlJdk9yiyRvAT7RNp0kSZIkCVyJL82kJI8ADgQeD2wDHAoc0jSUtPm2bR1AWsYdNvYDVXX1NIKsspcB96uqy5IEOInuCYNjgfs2TSZJkiRJAiziSzMlyRHAk4CLgI8CrwY+X1UfaRpMunHcjFlr2c6j1eiLqqqXTjPMKrqyqi4DqKpKchXwmKq6uHEuSZIkSdKIRXxptuwDXA78HfD3VXVJEguhWrOSPGeJU3PAnaeZRdpMvwTObR1iCq6bGP+3BXxJkiRJWlss4kszpKp2TfIA4CDga0l+AGyX5HZV9fOm4aTFLbkZM3Dc1FJIm+/iqvpQ6xBTsMPEzbYNxlV1TINMkiRJkqQxFvGlGVNVZwJnJvl/gUfQFfS/m+TLVbV/23TShqrqdYsdT7ITcMCU40ib44zWAabkODa82TY5liRJkiQ1ZhFfmlFVNQ+cApySZEtg77aJpOUluRNd4f7JdJuGDmGVs2bXZ8YHSbaqqqtGr59RVe9tE2tlLXWjbVyS12zKz0mSJEmSVodFfGmGJPl14PXAzsCZwKuq6pfArsBLgU83jCfdQJI70G3GfBDd+/Yk4HZVtUvTYNLGvZruRumCfwQeNXp9ENCLIv4mekTrAJIkSZI0ZBbxpdnyfuAE4G10hdG/TnIp8Ei6Ir601lwM/BvwEuDzVXVdkrMaZ5I2xdwy48lzfTe0+UqSJEnSmmIRX5ott6yqY0evX5/kIuDNwGFVdW3DXNJS/gg4kO4G1KeTnNA4j7Sp5pcZT57ru6HNV5IkSZLWFIv40myZLNRfWFXHNEkibYKq+ijw0SS3B/aja1Gya5K3Ah+oqvOaBpSWti7JNqxfhb4w3gJY1y6WJEmSJGloLOJLs2WyqLTF+LiqLm+WTFpGVV0GvAd4T5I7063OPxbYrWkwaWl3Ac4dG89NjIfEdjqSJEmS1JBFfGm27EhXRBovqCysZJ4Hdpp6ImkZSY6qqkPHj1XVj+n2dXhbm1TSJnntMud61V4mybqFlmxJHgncAriyqr4w+pGntsomSZIkSbKIL82Uqrpr6wzSZrpX6wDSCpkHtgSeBexA9yTJzEvyAOCEJPccFfLfBXwT+K0k76iqY6vq39umlCRJkqRhs4gvzZAkz5k4NA/8BPhqVV3aIJK0MTss8r69nns6aK2qqg+Nj5McALwI+Dv69RTJEcAfj22O/rOqelqSO9LNtRc3KyRJkiRpllnEl2bL9osc+03g9UmeP9b6QFortgS2w57amlFJ9gTeAJwB7FVVlzSOtNK2qKqvjY2/BFBVlyT5VaNMkiRJkqQxFvGlGVJVr1vseJLtgb8FLOJrrflBVf1F6xDS5kpyb+BNwC+Ap1TVBY0jrZatxwdV9dqx4W2mG0WSJEmStBiL+FIPVNWlSXq10aJ648etA0g30rfoNg4/A3hFkoXjc8B8VT29VbAVdmaSw6rq+hZBSbYEDgc+1y6WJEmSJGmBRXypB5LsRNcfX1pTqur/ges3zwxwLXBeVZ3TNJi0cXdvHWBKXgwcmeTfgPPpWmDdg64f/ktaBpMkSZIkdebm5637SbMiyce5YbH+9sCdgYOr6qzpp5KWluR2wKeAq4Fv061ivh9wDd171g2ZpTUgya3oblxcB1xQVZc3jiRJkiRJGnElvjRbjlrk2KXA+VV1zbTDSJvgncA7q+qk8YNJ9h2dO7BJKkkAJHnqIofvv9A+qKqOnW4iSZIkSdIki/jSbNmvqg5tHULaDLtW1Q2KhFV1UpI/axFI0gbmFjl2M+BZwA6ARXxJkiRJaswivjRb7tU6gLSZtljm3LqppZC0qKr60Pg4yQHAy+h64r9t0b8kSZIkSZoqi/jSbNkhyXOWOllVx0wzjLQJzk9ycFUdN34wyTOBsxtlkjQhyZ7AG4AzgL2q6pLGkSRJkiRJIxbxpdmyJbAdi7c/kNai5wMfTnIo8C261fe7Af8BHNAymCRIcm/gTcAvgKdU1QWNI0mSJEmSJszNz8+3ziBpEyX5YlXt2TqHtKmSnFhVT0q3S+auwDzwvao6v3E0SUCSa4Dz6Fbgj38pnAPmq+rpTYJJkiRJkq7nSnxptvy4dQBpM90BoKoKqMZZJN3Q3VsHkCRJkiQtz5X40oxLchvgScABVbVX6zzSuCQ/BE5Y6nxVvXSKcSRNSHKL5c5X1eXTyiJJkiRJWpwr8aUZlGQb4A+Ag4BHAR8DDm8aSlrcL4FzW4eQtKRz6drojO+1sjCeB3ZqEUqSJEmStJ5FfGmGJPkD4MnA7wGnAB8E7lZVz2gYS1rOxVX1odYhJC1pl6q6unUISZIkSdLStmgdQNJm+QRwP+DJVbV/VX0SuKZxJmk5Z7QOIGlZn28dQJIkSZK0PFfiS7NlR+AA4C2jXvgnAFu3jSQtraoOa51B0rLmNv4jkiRJkqSW3NhWmlFJdqHriX8g8D/A+6vq3W1TSZJmSZJL6NqzLaqq9p9eGkmSJEnSYlyJL82QJE+dOPR94I3A9sDrAYv4kqTN8WPg6NYhJEmSJElLs4gvzZbJtgfzwJZ0LXbcmFCStLl+XlVfah1CkiRJkrQ0i/jSDKmqD42PkxwAvAj4O+BtTUJJkmbZPyx1IsnNq+pX0wwjSZIkSbohe+JLMyjJnsAbgDOAw6vqksaRJEk9kGQd8Pt0e648sqru0jiSJEmSJA2eK/GlGZLk3sCbgF8AT6mqCxpHkiT1QJJH0G2U/nhgG+BQ4JCmoSRJkiRJgCvxpZmS5BrgPLoV+OMf3jlgvqqe3iSYJGkmJTkCeBJwEfBR4ETg81V1/6bBJEmSJEnXcyW+NFvu3jqAJKlX9gEup9tb5e+r6pIkrvCQJEmSpDXElfiSJEkDluQBdD3w9wd+ANwV+K2q+nnDWJIkSZKkEYv4kiRJIskc8Ai6gv7jgC9X1f5tU0mSJEmSLOJLkiRpA0m2BPauqk+3ziJJkiRJQ2dPfEmSpIFK8uvA64GdgTOBV1XVL4FdgZcCFvElSZIkqTGL+JIkScP1fuAE4G3Ak4C/TnIp8Ei6Ir4kSZIkqTGL+JIkScN1y6o6dvT69UkuAt4MHFZV1zbMJUmSJEkasYgvSZI0XJOF+gur6pgmSSRJkiRJi7KIL0mSNFzrkmwDzI3GW4yPq+ryZskkSZIkSYBFfEmSpCHbETiX9UV8xsbzwE4tQkmSJEmS1pubn59vnUGSJEmSJEmSJC1ii9YBJEmS1EaSF06Mdx97fdT0E0mSJEmSJlnElyRJGq4/mBj/5djre00ziCRJkiRpcRbxJUmShmtuI2NJkiRJUmMW8SVJkoZrcnMkN0uSJEmSpDXmZq0DSJIkqZntkuwzNt52NJ4Dtm2USZIkSZI0xiK+JEnScJ0B7Dc2PnNsfOb040iSJEmSJs3Nz/vUtCRJkiRJkiRJa5Er8SVJkgYqya7Au4CdgdOBF1TVxW1TSZIkSZLGubGtJEnScB0NHA7sCnwcOKJtHEmSJEnSJFfiS5IkDde6qjp19PrEJIc2TSNJkiRJugFX4kuSJA3XdRsZS5IkSZIac2NbSZKkgUryQ+CE0XAOOGBsTFW9tEUuSZIkSdJ6ttORJEkarleN/rs9sCVdj/yrgMuaJZIkSZIkbcAiviRJ0nB9DHgPsDtwDt1q/N8ETgWe3zCXJEmSJGnEIr4kSdJwvRn4VlU9dfxgkhcBRwLPapJKkiRJknQ9N7aVJEkarj2q6ojJg1V1JLBbgzySJEmSpAkW8SVJkobr2mXOXTe1FJIkSZKkJVnElyRJGq6fJnnk5MEkewM/nn4cSZIkSdIke+JLkiQN1wuBk5IUcBawDngQcFdgr4a5JEmSJEkjc/Pz860zSJIkqZEkc8D/BnYF5oHvASdXlV8SJUmSJGkNsIgvSZIkSZIkSdIaZU98SZIkSZIkSZLWKIv4kiRJkiRJkiStURbxJUmSJEmSJElaoyziS5IkSZIkSZK0Rv3/gJWdmT5/P8IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1872x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制因子正交后的相关性热力图\n",
    "fig=plt.figure(figsize=(26,18))\n",
    "#计算对称正交之后的相关系数矩阵\n",
    "relations= get_relations(factors5.iloc[:,:-2])  \n",
    "sns.heatmap(relations,annot=True,linewidths=0.05,\n",
    "            linecolor='white',annot_kws={'size':8,'weight':'bold'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、打分法计算预期收益\n",
    "\n",
    "### 5.1计算ICIR权重\n",
    "\n",
    "在利用IR加权法计算权重时，各因子权重值为各因子在滚动时间内平均IR值在所有因子在滚动时间内平均IR值之和中的占比，因子在滚动时间内平均IR值越大，说明该因子在选股时作用越大，权重也越大，反之亦然。在利用IR计算权重时需注意的是：在滚动计算IC序列标准差时，起始日期得到的是缺失值，所以对于起始日期的IR值我们用该时点的IC值来代替， 得到的权重也为IC值计算的IC权重。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def get_next_ret(factor: pd.DataFrame, keep_last_term: bool = False, last_term_next_date: str = None) -> pd.Series:\n",
    "    '''\n",
    "    keep_last_term:是否保留最后一期数据\n",
    "    last_term_next_date:如果keep_last_term=True,则此参数为计算最后一期下期收益时的截止时间,必须时交易日\n",
    "    '''\n",
    "\n",
    "    securities = factor.index.levels[1].tolist()  # 股票代码\n",
    "    periods = [i.strftime('%Y-%m-%d') for i in factor.index.levels[0]]  # 日期\n",
    "\n",
    "    if keep_last_term:\n",
    "        end = last_term_next_date\n",
    "        periods = periods + [end]\n",
    "\n",
    "        if not end:\n",
    "            raise ValueError('如果keep_last_term=True,则必须有last_term_next_date参数')\n",
    "\n",
    "    close = pd.concat([get_price(securities, end_date=i, count=1, fields='close', panel=False)\n",
    "                       for i in periods])\n",
    "\n",
    "    close = pd.pivot_table(close, index='time', columns='code', values='close')\n",
    "    ret = close.pct_change().shift(-1)\n",
    "    ret = ret.iloc[:-1]\n",
    "    return ret.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取下期收益率\n",
    "next_ret = get_next_ret(factors5,True,'2020-10-14')\n",
    "\n",
    "factors5['NEXT_RET'] = next_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据IR计算因子权重\n",
    "\n",
    "# step1:计算rank_IC\n",
    "\n",
    "\n",
    "def calc_rank_IC(factor: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    factor_col = [x for x in factor.columns if x not in [\n",
    "        'INDUSTRY_CODE', 'market_cap', 'NEXT_RET']]\n",
    "\n",
    "    IC = factor.groupby(level='date').apply(lambda x: [st.spearmanr(\n",
    "        x[factor], x['NEXT_RET'])[0] for factor in factor_col])\n",
    "    \n",
    "    return pd.DataFrame(IC.tolist(), index=IC.index, columns=factor_col)\n",
    "\n",
    "\n",
    "## step2: 计算IR权重\n",
    "def IR_weight(factor: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    data_ = factor.copy()\n",
    "    # 计算ic值，得到ic的\n",
    "    IC = calc_rank_IC(data_)\n",
    "    \n",
    "    # 计算ic的绝对值\n",
    "    abs_IC = IC.abs()\n",
    "    # rolling为移动窗口函数,滚动12个月\n",
    "    rolling_ic = abs_IC.rolling(12, min_periods=1).mean()\n",
    "    # 当滚动计算标准差时，起始日期得到的是缺失值，所以算完权重后，起始日期的值任用原值IC代替\n",
    "    rolling_ic_std = abs_IC.rolling(12, min_periods=1).std()\n",
    "    IR = rolling_ic / rolling_ic_std  # 计算IR值\n",
    "    IR.iloc[0,:] = rolling_ic.iloc[0,:]\n",
    "    weight = IR.div(IR.sum(axis=1), axis=0)  # 计算IR权重,按行求和,按列相除\n",
    "\n",
    "    return weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取权重\n",
    "weights = IR_weight(factors5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取因子名称\n",
    "factor_names = [name for name in factors5.columns if name not in [\n",
    "    'INDUSTRY_CODE', 'market_cap', 'NEXT_RET']]\n",
    "\n",
    "# 计算因子分数\n",
    "factors5['SCORE'] = (factors5[factor_names].mul(weights)).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据储存\n",
    "factors5.to_csv('factors5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NATURAL_LOG_OF_MARKET_CAP</th>\n",
       "      <th>ROE_TTM</th>\n",
       "      <th>ROC60</th>\n",
       "      <th>TOTAL_PROFIT_GROWTH_RATE</th>\n",
       "      <th>BOOK_TO_PRICE_RATIO</th>\n",
       "      <th>VOL60</th>\n",
       "      <th>NET_PROFIT_GROWTH_RATE</th>\n",
       "      <th>OPERATING_REVENUE_GROWTH_RATE</th>\n",
       "      <th>ROA_TTM</th>\n",
       "      <th>VOL20</th>\n",
       "      <th>ROC20</th>\n",
       "      <th>EPTTM</th>\n",
       "      <th>SPTTM</th>\n",
       "      <th>SUE0</th>\n",
       "      <th>SUR0</th>\n",
       "      <th>DELTAROE</th>\n",
       "      <th>DELTAROA</th>\n",
       "      <th>ILLIQ</th>\n",
       "      <th>ATR1M</th>\n",
       "      <th>ATR3M</th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>market_cap</th>\n",
       "      <th>NEXT_RET</th>\n",
       "      <th>SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-29</th>\n",
       "      <th>000001.XSHE</th>\n",
       "      <td>-0.025422</td>\n",
       "      <td>-0.057396</td>\n",
       "      <td>-0.041573</td>\n",
       "      <td>-0.053783</td>\n",
       "      <td>-0.001899</td>\n",
       "      <td>-0.050937</td>\n",
       "      <td>-0.039381</td>\n",
       "      <td>-0.005699</td>\n",
       "      <td>-0.038712</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>0.020523</td>\n",
       "      <td>-0.026802</td>\n",
       "      <td>-0.067861</td>\n",
       "      <td>-0.020078</td>\n",
       "      <td>-0.076909</td>\n",
       "      <td>0.017329</td>\n",
       "      <td>-0.010752</td>\n",
       "      <td>-0.063413</td>\n",
       "      <td>0.094269</td>\n",
       "      <td>0.112668</td>\n",
       "      <td>801190</td>\n",
       "      <td>673.8791</td>\n",
       "      <td>0.035613</td>\n",
       "      <td>-0.011532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.XSHE</th>\n",
       "      <td>-0.077438</td>\n",
       "      <td>-0.053004</td>\n",
       "      <td>-0.032169</td>\n",
       "      <td>-0.005922</td>\n",
       "      <td>0.035340</td>\n",
       "      <td>0.056205</td>\n",
       "      <td>-0.007953</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>-0.021120</td>\n",
       "      <td>-0.041890</td>\n",
       "      <td>0.016944</td>\n",
       "      <td>0.030378</td>\n",
       "      <td>0.034553</td>\n",
       "      <td>-0.034932</td>\n",
       "      <td>0.039879</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>-0.000329</td>\n",
       "      <td>-0.051645</td>\n",
       "      <td>0.136641</td>\n",
       "      <td>0.074863</td>\n",
       "      <td>801180</td>\n",
       "      <td>1026.9526</td>\n",
       "      <td>0.008915</td>\n",
       "      <td>-0.007426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.XSHE</th>\n",
       "      <td>0.008981</td>\n",
       "      <td>0.007922</td>\n",
       "      <td>-0.076063</td>\n",
       "      <td>0.025250</td>\n",
       "      <td>-0.063062</td>\n",
       "      <td>-0.022548</td>\n",
       "      <td>0.042210</td>\n",
       "      <td>0.018641</td>\n",
       "      <td>-0.009017</td>\n",
       "      <td>0.127766</td>\n",
       "      <td>0.080858</td>\n",
       "      <td>-0.036605</td>\n",
       "      <td>0.031204</td>\n",
       "      <td>0.006564</td>\n",
       "      <td>-0.101361</td>\n",
       "      <td>0.017244</td>\n",
       "      <td>0.013028</td>\n",
       "      <td>-0.040314</td>\n",
       "      <td>-0.010221</td>\n",
       "      <td>-0.050514</td>\n",
       "      <td>801180</td>\n",
       "      <td>113.7653</td>\n",
       "      <td>0.083700</td>\n",
       "      <td>0.000890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000012.XSHE</th>\n",
       "      <td>-0.009324</td>\n",
       "      <td>-0.032775</td>\n",
       "      <td>0.011619</td>\n",
       "      <td>0.020682</td>\n",
       "      <td>0.050878</td>\n",
       "      <td>0.024256</td>\n",
       "      <td>0.028241</td>\n",
       "      <td>0.030283</td>\n",
       "      <td>-0.021500</td>\n",
       "      <td>-0.071160</td>\n",
       "      <td>0.005995</td>\n",
       "      <td>0.049959</td>\n",
       "      <td>-0.086777</td>\n",
       "      <td>0.029233</td>\n",
       "      <td>0.122518</td>\n",
       "      <td>0.029718</td>\n",
       "      <td>0.054647</td>\n",
       "      <td>-0.022774</td>\n",
       "      <td>0.048842</td>\n",
       "      <td>-0.018290</td>\n",
       "      <td>801060</td>\n",
       "      <td>223.1420</td>\n",
       "      <td>0.069024</td>\n",
       "      <td>0.015042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000021.XSHE</th>\n",
       "      <td>0.032012</td>\n",
       "      <td>0.030933</td>\n",
       "      <td>-0.124656</td>\n",
       "      <td>-0.011902</td>\n",
       "      <td>0.012273</td>\n",
       "      <td>-0.009027</td>\n",
       "      <td>0.066864</td>\n",
       "      <td>-0.004095</td>\n",
       "      <td>-0.012922</td>\n",
       "      <td>-0.015141</td>\n",
       "      <td>0.088950</td>\n",
       "      <td>0.068526</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>0.031011</td>\n",
       "      <td>0.068233</td>\n",
       "      <td>-0.068963</td>\n",
       "      <td>0.028277</td>\n",
       "      <td>0.010861</td>\n",
       "      <td>0.013923</td>\n",
       "      <td>-0.010707</td>\n",
       "      <td>801100</td>\n",
       "      <td>114.6892</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>-0.009551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        NATURAL_LOG_OF_MARKET_CAP    ...        SCORE\n",
       "date       code                                      ...             \n",
       "2010-01-29 000001.XSHE                  -0.025422    ...    -0.011532\n",
       "           000002.XSHE                  -0.077438    ...    -0.007426\n",
       "           000009.XSHE                   0.008981    ...     0.000890\n",
       "           000012.XSHE                  -0.009324    ...     0.015042\n",
       "           000021.XSHE                   0.032012    ...    -0.009551\n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factors5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、分层抽样指数增强策略实现\n",
    "\n",
    "## 6.1 策略概述\n",
    "\n",
    "市值和行业是很重要的风险因子，分层抽样策略的核心是使投资组合在这两个风险维度上与基准指数保持一致，然后在市值、行业属性比较相似的若干只股票里优选一只预期收益最高的进行投资，以获取超额收益。  \n",
    "\n",
    "策略中所使用的`行业分类`如下：\n",
    "以申万34个一级行业为蓝本\n",
    "\n",
    "`策略步骤`：  \n",
    "**step1**:将基准指数成份股按以上行业划分成34个子集，在每个子集中用市值因子将股票划分为数目相等的三组；  \n",
    "**step2**:计算每个小组内所有股票在基准指数中的总权重；  \n",
    "**step3**:在每个小组中选择预期收益（打分法）最高的一只股票，令它在投资组合中的权重等于它所处小组的权重。这样就能选出包含102只股票的分层抽样组合。\n",
    "\n",
    "**本策略的基准指数选`沪深300指数`，且在基准指数`成份股内`选股。**\n",
    "\n",
    "+ 参考于华泰证券《指数增强方法汇总及实例——量化多因子指数增强策略实证》中的第一个简单策略。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weighs(symbol:str,start:str,end:str,method:str='cons')->pd.DataFrame:\n",
    "    '''\n",
    "    获取月度指数成份权重\n",
    "    --------\n",
    "        mehtod:ind 输出 行业权重\n",
    "               cons 输出 成份股权重\n",
    "    '''\n",
    "    periods = GetTradePeriod(start, end, 'ME')\n",
    "    \n",
    "    ser_dic = {}\n",
    "    if method == 'ind':\n",
    "        for d in periods:\n",
    "\n",
    "            # 获取当日成份及权重\n",
    "            index_w = get_index_weights(symbol,date=d)\n",
    "            # 获取行业\n",
    "            index_w['ind'] = get_stock_ind(index_w.index.tolist(),d)\n",
    "            # 计算行业所占权重\n",
    "            weight = index_w.groupby('ind')['weight'].sum() / 100\n",
    "\n",
    "            ser_dic[d] = weight\n",
    "\n",
    "        ser = pd.concat(ser_dic,names=['date','industry']).reset_index()\n",
    "        ser['date'] = pd.to_datetime(ser['date'])\n",
    "        return ser.set_index(['date','industry'])\n",
    "    \n",
    "    elif method == 'cons':\n",
    "        \n",
    "        df = pd.concat([get_index_weights(symbol,date = d) for d in periods])\n",
    "        df.drop(columns='display_name',inplace=True)\n",
    "        \n",
    "        df.set_index('date',append=True,inplace=True)\n",
    "        df = df.swaplevel()\n",
    "        df['weight'] = df['weight'] / 100\n",
    "        return df\n",
    "            \n",
    "\n",
    "\n",
    "def get_group(ser:pd.Series,N:int=3,ascend:bool=True)->pd.Series:\n",
    "    '''默认分三组 升序'''\n",
    "    ranks = ser.rank(ascending=ascend)\n",
    "    label = ['G'+str(i) for i in range(1,N + 1)]\n",
    "    \n",
    "    return pd.cut(ranks,bins= N,labels=label) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NATURAL_LOG_OF_MARKET_CAP</th>\n",
       "      <th>ROE_TTM</th>\n",
       "      <th>ROC60</th>\n",
       "      <th>TOTAL_PROFIT_GROWTH_RATE</th>\n",
       "      <th>BOOK_TO_PRICE_RATIO</th>\n",
       "      <th>VOL60</th>\n",
       "      <th>NET_PROFIT_GROWTH_RATE</th>\n",
       "      <th>OPERATING_REVENUE_GROWTH_RATE</th>\n",
       "      <th>ROA_TTM</th>\n",
       "      <th>VOL20</th>\n",
       "      <th>ROC20</th>\n",
       "      <th>EPTTM</th>\n",
       "      <th>SPTTM</th>\n",
       "      <th>SUE0</th>\n",
       "      <th>SUR0</th>\n",
       "      <th>DELTAROE</th>\n",
       "      <th>DELTAROA</th>\n",
       "      <th>ILLIQ</th>\n",
       "      <th>ATR1M</th>\n",
       "      <th>ATR3M</th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>market_cap</th>\n",
       "      <th>NEXT_RET</th>\n",
       "      <th>SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-09-30</th>\n",
       "      <th>603799.XSHG</th>\n",
       "      <td>-0.030456</td>\n",
       "      <td>-0.062129</td>\n",
       "      <td>-0.070515</td>\n",
       "      <td>0.084432</td>\n",
       "      <td>-0.042606</td>\n",
       "      <td>0.100219</td>\n",
       "      <td>0.082309</td>\n",
       "      <td>-0.039478</td>\n",
       "      <td>-0.074763</td>\n",
       "      <td>-0.038108</td>\n",
       "      <td>-0.003796</td>\n",
       "      <td>-0.011294</td>\n",
       "      <td>-0.003044</td>\n",
       "      <td>-0.000212</td>\n",
       "      <td>-0.041562</td>\n",
       "      <td>0.023159</td>\n",
       "      <td>-0.009414</td>\n",
       "      <td>-0.018132</td>\n",
       "      <td>-0.052855</td>\n",
       "      <td>-0.071029</td>\n",
       "      <td>801050</td>\n",
       "      <td>395.9036</td>\n",
       "      <td>0.074373</td>\n",
       "      <td>-0.010918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603833.XSHG</th>\n",
       "      <td>0.035622</td>\n",
       "      <td>0.047712</td>\n",
       "      <td>0.060469</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>-0.020560</td>\n",
       "      <td>0.066126</td>\n",
       "      <td>0.011882</td>\n",
       "      <td>0.066869</td>\n",
       "      <td>0.099462</td>\n",
       "      <td>-0.059059</td>\n",
       "      <td>-0.000596</td>\n",
       "      <td>-0.001691</td>\n",
       "      <td>0.039675</td>\n",
       "      <td>0.014614</td>\n",
       "      <td>-0.015029</td>\n",
       "      <td>0.051586</td>\n",
       "      <td>-0.088533</td>\n",
       "      <td>0.097730</td>\n",
       "      <td>0.013501</td>\n",
       "      <td>-0.064811</td>\n",
       "      <td>801140</td>\n",
       "      <td>641.9369</td>\n",
       "      <td>0.089171</td>\n",
       "      <td>0.019785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603899.XSHG</th>\n",
       "      <td>0.002282</td>\n",
       "      <td>0.072179</td>\n",
       "      <td>0.022306</td>\n",
       "      <td>0.016043</td>\n",
       "      <td>-0.019734</td>\n",
       "      <td>0.063239</td>\n",
       "      <td>0.005220</td>\n",
       "      <td>0.075474</td>\n",
       "      <td>0.143531</td>\n",
       "      <td>-0.062556</td>\n",
       "      <td>-0.041486</td>\n",
       "      <td>-0.052774</td>\n",
       "      <td>0.039734</td>\n",
       "      <td>-0.008342</td>\n",
       "      <td>-0.031873</td>\n",
       "      <td>0.002782</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.128973</td>\n",
       "      <td>0.005955</td>\n",
       "      <td>-0.083852</td>\n",
       "      <td>801140</td>\n",
       "      <td>629.8161</td>\n",
       "      <td>0.123399</td>\n",
       "      <td>0.015803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603986.XSHG</th>\n",
       "      <td>0.027773</td>\n",
       "      <td>-0.152252</td>\n",
       "      <td>-0.141137</td>\n",
       "      <td>0.098222</td>\n",
       "      <td>0.009774</td>\n",
       "      <td>-0.009663</td>\n",
       "      <td>0.093830</td>\n",
       "      <td>0.106675</td>\n",
       "      <td>0.026121</td>\n",
       "      <td>0.016322</td>\n",
       "      <td>-0.003285</td>\n",
       "      <td>0.013823</td>\n",
       "      <td>-0.040854</td>\n",
       "      <td>0.008571</td>\n",
       "      <td>-0.057250</td>\n",
       "      <td>-0.049125</td>\n",
       "      <td>-0.145776</td>\n",
       "      <td>-0.044730</td>\n",
       "      <td>0.116384</td>\n",
       "      <td>-0.001668</td>\n",
       "      <td>801080</td>\n",
       "      <td>816.3362</td>\n",
       "      <td>0.064294</td>\n",
       "      <td>-0.006480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603993.XSHG</th>\n",
       "      <td>-0.051922</td>\n",
       "      <td>-0.065619</td>\n",
       "      <td>-0.010722</td>\n",
       "      <td>-0.137293</td>\n",
       "      <td>-0.027079</td>\n",
       "      <td>-0.026566</td>\n",
       "      <td>0.031637</td>\n",
       "      <td>0.177514</td>\n",
       "      <td>-0.014475</td>\n",
       "      <td>-0.056039</td>\n",
       "      <td>-0.030212</td>\n",
       "      <td>-0.039328</td>\n",
       "      <td>0.056297</td>\n",
       "      <td>-0.004894</td>\n",
       "      <td>0.008176</td>\n",
       "      <td>0.014167</td>\n",
       "      <td>-0.036459</td>\n",
       "      <td>-0.037108</td>\n",
       "      <td>-0.048525</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>801050</td>\n",
       "      <td>803.4917</td>\n",
       "      <td>0.045699</td>\n",
       "      <td>-0.026097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        NATURAL_LOG_OF_MARKET_CAP    ...        SCORE\n",
       "date       code                                      ...             \n",
       "2020-09-30 603799.XSHG                  -0.030456    ...    -0.010918\n",
       "           603833.XSHG                   0.035622    ...     0.019785\n",
       "           603899.XSHG                   0.002282    ...     0.015803\n",
       "           603986.XSHG                   0.027773    ...    -0.006480\n",
       "           603993.XSHG                  -0.051922    ...    -0.026097\n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据读取\n",
    "factors5 = pd.read_csv('factors5.csv',index_col=[0,1],parse_dates=[0],dtype={'INDUSTRY_CODE':str})\n",
    "factors5.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stratified_sampling(symbol: str, START_DATE: str, END_DATE: str, factors: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    factors_ = factors.copy()\n",
    "    ind_weight = get_weighs(symbol, START_DATE, END_DATE)\n",
    "\n",
    "    # 市值等量分三组\n",
    "    k1 = [pd.Grouper(level='date'),\n",
    "          pd.Grouper(key='INDUSTRY_CODE')]\n",
    "\n",
    "    factors_['GROUP'] = factors_.groupby(\n",
    "        k1)['market_cap'].apply(lambda x: get_group(x, 3))\n",
    "\n",
    "    # 获取每组得分最大的\n",
    "    k2 = [pd.Grouper(level='date'),\n",
    "          pd.Grouper(key='INDUSTRY_CODE'),\n",
    "          pd.Grouper(key='GROUP')]\n",
    "\n",
    "    industry_kfold_stock = factors_.groupby(\n",
    "        k2)['SCORE'].apply(lambda x: x.idxmax()[1])\n",
    "    \n",
    "    # 格式调整\n",
    "    industry_kfold_stock = industry_kfold_stock.reset_index()\n",
    "    industry_kfold_stock = industry_kfold_stock.set_index(['date', 'SCORE'])\n",
    "    industry_kfold_stock.index.names = ['date', 'code']\n",
    "    \n",
    "    # 加入权重\n",
    "    industry_kfold_stock['weight'] = ind_weight['weight']\n",
    "    \n",
    "    # 令权重加总为1\n",
    "    industry_kfold_stock['w'] = industry_kfold_stock.groupby(\n",
    "        level='date')['weight'].transform(lambda x: x / x.sum())\n",
    "    \n",
    "    industry_kfold_stock['NEXT_RET'] = factors['NEXT_RET']\n",
    "\n",
    "    return industry_kfold_stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取分层数据\n",
    "result_df = stratified_sampling('000300.XSHG', START_DATE, END_DATE, factors5[[\n",
    "                                'INDUSTRY_CODE', 'market_cap', 'SCORE', 'NEXT_RET']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>weight</th>\n",
       "      <th>w</th>\n",
       "      <th>NEXT_RET</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-29</th>\n",
       "      <th>600816.XSHG</th>\n",
       "      <td>801190</td>\n",
       "      <td>G1</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.005726</td>\n",
       "      <td>0.053279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000783.XSHE</th>\n",
       "      <td>801190</td>\n",
       "      <td>G2</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.023425</td>\n",
       "      <td>-0.004335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601601.XSHG</th>\n",
       "      <td>801190</td>\n",
       "      <td>G3</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.070276</td>\n",
       "      <td>0.074182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600638.XSHG</th>\n",
       "      <td>801180</td>\n",
       "      <td>G1</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.006767</td>\n",
       "      <td>0.034389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600675.XSHG</th>\n",
       "      <td>801180</td>\n",
       "      <td>G2</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.010411</td>\n",
       "      <td>0.026132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       INDUSTRY_CODE GROUP  weight         w  NEXT_RET\n",
       "date       code                                                       \n",
       "2010-01-29 600816.XSHG        801190    G1  0.0011  0.005726  0.053279\n",
       "           000783.XSHE        801190    G2  0.0045  0.023425 -0.004335\n",
       "           601601.XSHG        801190    G3  0.0135  0.070276  0.074182\n",
       "           600638.XSHG        801180    G1  0.0013  0.006767  0.034389\n",
       "           600675.XSHG        801180    G2  0.0020  0.010411  0.026132"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 储存,用于回测\n",
    "result_df.to_csv('../../result_df.csv')\n",
    "result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark_returns</th>\n",
       "      <th>returns</th>\n",
       "      <th>excess_returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04 16:00:00</th>\n",
       "      <td>-0.011313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05 16:00:00</th>\n",
       "      <td>-0.003255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06 16:00:00</th>\n",
       "      <td>-0.009495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07 16:00:00</th>\n",
       "      <td>-0.029147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08 16:00:00</th>\n",
       "      <td>-0.026722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     benchmark_returns  returns  excess_returns\n",
       "time                                                           \n",
       "2010-01-04 16:00:00          -0.011313      0.0        0.011442\n",
       "2010-01-05 16:00:00          -0.003255      0.0        0.003266\n",
       "2010-01-06 16:00:00          -0.009495      0.0        0.009586\n",
       "2010-01-07 16:00:00          -0.029147      0.0        0.030022\n",
       "2010-01-08 16:00:00          -0.026722      0.0        0.027456"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据result_df文件中生成的权重及股票名单进行回测\n",
    "# 获取回测结果\n",
    "gt = get_backtest('b5f5173d64f8fb49496c2c309c0635e3')\n",
    "\n",
    "algorithm = pd.DataFrame(gt.get_results()).set_index('time')\n",
    "algorithm['excess_returns'] = (\n",
    "    algorithm['returns'] + 1) / (algorithm['benchmark_returns'] + 1) - 1\n",
    "\n",
    "algorithm.index = pd.to_datetime(algorithm.index)\n",
    "algorithm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6078e1c160>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = 'serif'\n",
    "algorithm.plot.line(figsize=(18, 6), title='分层抽样指数增强策略', color=[\n",
    "                    'LightGrey', 'LightCoral', 'GoldenRod'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "algorithm_return                                      1.72765\n",
       "algorithm_volatility                                 0.232204\n",
       "alpha                                               0.0717342\n",
       "annual_algo_return                                   0.100361\n",
       "annual_bm_return                                    0.0283163\n",
       "avg_excess_return                                  0.00027744\n",
       "avg_position_days                                     293.056\n",
       "avg_trade_return                                    0.0395027\n",
       "benchmark_return                                     0.340397\n",
       "benchmark_volatility                                 0.231306\n",
       "beta                                                 0.973403\n",
       "day_win_ratio                                        0.531834\n",
       "excess_return                                         1.03495\n",
       "excess_return_max_drawdown                          0.0662488\n",
       "excess_return_max_drawdown_period    [2018-07-19, 2018-11-27]\n",
       "excess_return_sharpe                                 0.525077\n",
       "information                                           1.26139\n",
       "lose_count                                               2202\n",
       "max_drawdown                                         0.423604\n",
       "max_drawdown_period                  [2015-06-08, 2016-01-28]\n",
       "max_leverage                                                0\n",
       "period_label                                          2020-10\n",
       "profit_loss_ratio                                     1.48329\n",
       "sharpe                                               0.259949\n",
       "sortino                                              0.332465\n",
       "trading_days                                             2623\n",
       "treasury_return                                       0.43211\n",
       "turnover_rate                                        0.019175\n",
       "win_count                                                2288\n",
       "win_ratio                                            0.509577\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 风险指标如下\n",
    "pd.Series(gt.get_risk()).iloc[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、多因子线性优化模型指数增强策略实现\n",
    "\n",
    "## 7.1 策略概述\n",
    "`多因子线性优化模型指数增强策略`的核心是控制投资组合在更多的风险因子上的暴露与基准指数一致，以期获得更小的跟踪误差。常见的风险控制形式主要包括风险暴露约束、行业暴露约束、相对于基准跟踪误差约束、个股权重约束等，这些约束条件都能有效地控制组合相对基准指数的偏离，使组合能稳定的战胜基准指数。该策略一般需要通过求解二次优化或非线性优化问题来构建最优组合。\n",
    "\n",
    "策略的组合优化模型形式如下所示：  \n",
    "\n",
    "目标函数：$$max( r^{T}w)$$\n",
    "约束条件：$$s_{l}\\leqslant X(w-w_{b})\\leqslant s_{h}$$\n",
    "\n",
    "$$h_{l}\\leqslant H(w-w_{b})\\leqslant h_{h}$$\n",
    "\n",
    "$$w_{l}\\leqslant w-w_{b} \\leqslant w_{h}$$\n",
    "\n",
    "$$b_{l}\\leqslant B_{b}w \\leqslant b_{h}$$\n",
    "\n",
    "$$0 \\leqslant w \\leqslant l $$\n",
    "\n",
    "$$I^{T}w=1$$\n",
    "\n",
    "该优化问题的目标函数为最大化组合收益，其中$r^{T}w$为组合预期收益，w为待求解的股票权重向量，r为前面计算得到的每股综合得分。模型的约束条件包括在风格因子上的偏离度、行业偏离度、个股偏离度、成分股权重占比控制、个股权重上限控制，具体解释如下：  \n",
    "\n",
    "+ 约束条件1控制了组合相对于基准指数的风格暴露，X为股票对风格因子的因子暴露矩阵，$w_{b}$为基准指数成分股的权重向量，$s_{l},s_{h}$分别为风格因子相对暴露的下限和上限；\n",
    "\n",
    "\n",
    "+ 约束条件2限制了组合相对于基准指数的行业偏离，H为股票的行业暴露矩阵，当股票i属于行业j时，$H_{ji}$为1，否则为0，$h_{l},h_{h}$分别为组合行业偏离的下限和上限，此处行业采用前文的32个行业分类方法；\n",
    "\n",
    "\n",
    "+ 约束条件3限制了个股相对于基准指数成分股的偏离，$w_{l},w_{h}$分别为个股偏离的下限和上限；\n",
    "\n",
    "\n",
    "+ 约束条件4限制了组合在成分股内权重的占比下限及上限，$B_{b}$为个股是否属于基准指数成分股的0-1向量，$b_{l},b_{h}$分别为成分股内权重占比的上限及下限；\n",
    "\n",
    "\n",
    "+ 约束条件5限制了卖空，并且限制了个股权限的上限l；\n",
    "\n",
    "\n",
    "+ 约束条件6要求权重和为1，即组合始终满仓运作。\n",
    "\n",
    "`注：`该组合优化模型没有采用二次项的跟踪误差约束（$\\left ( w-w_{b} \\right )^{T}\\Sigma \\left ( w-w_{b} \\right )\\leqslant \\frac{TE^{2}}{250}$,$TE^{2}$为组合的预期年化跟踪误差的上限）来控制组合对基准的偏离，而是用的约束条件3中的个股相对于基准指数成分股的偏离度，原因是：  \n",
    "1、直接用跟踪误差作为约束条件进行风险控制需要估计协方差矩阵，跟踪误差是否控制成功依赖于协方差矩阵估计的准确性，而协方差矩阵的估计本身受很多假设条件的限制，估计较为困难，不稳定性较大，而用个股相对于基准指数成分股的偏离度来控制跟踪误差更为直接，个股偏离度越小，对基准指数的跟踪误差也就越小；  \n",
    "2、跟踪误差约束是二次约束，而偏离度约束是线性约束，后者比前者的求解更高效。\n",
    "\n",
    "***本策略的基准指数选`沪深300指数`，且在基准指数成份股内选股，所以约束条件4不用设置。***\n",
    "\n",
    "+ 参考：天风证券《基于自适应风险控制的指数增强策略》中的静态指数增强策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimization_weight(df: pd.DataFrame):\n",
    "    '''\n",
    "    权重优化\n",
    "    '''\n",
    "    df_ = df.copy()\n",
    "    index_weight = df_['weight'].values\n",
    "    score = df_['SCORE'].values\n",
    "    f = df_.drop(columns=['SCORE', 'weight']).T.values\n",
    "\n",
    "    length = len(df_)\n",
    "    \n",
    "    # 目标函数\n",
    "    def func(w): return - w.dot(score)\n",
    "\n",
    "    # 1.构建约束条件1和2的不等式右边\n",
    "    # 2.构建约束条件1和2的不等式左边\n",
    "    # 3.构建约束条件3个股相对于基准指数成分股的偏离，最多不超过0.5%，不等式右\n",
    "    # 4.构建约束条件6组合权重之和为1\n",
    "    cons = (\n",
    "        {'type': 'ineq', 'fun': lambda w: 1.05 *\n",
    "            f.dot(index_weight) - f.dot(w)},\n",
    "        {'type': 'ineq', 'fun': lambda w: f.dot(\n",
    "            w) - 0.95 * f.dot(index_weight)},\n",
    "        {'type': 'ineq', 'fun': lambda w: 0.05 *\n",
    "         np.ones(length) - (w - index_weight)},\n",
    "        {'type': 'eq', 'fun': lambda w: np.sum(w) - 1},\n",
    "    )\n",
    "\n",
    "    # 建立约束条件5，个股权重上限，及权重的取值范围\n",
    "    limit = tuple((0, 1.5) for x in range(length))\n",
    "    \n",
    "    # 确定初始权重，为等权重\n",
    "    w0 = index_weight  \n",
    "    \n",
    "    # 利用递归最小二乘求解最优权重\n",
    "    res = minimize(func, w0, method='SLSQP', bounds=limit,\n",
    "                   constraints=cons)  \n",
    "    \n",
    "    if res['success'] != True:\n",
    "        print(df.name)\n",
    "\n",
    "    return pd.Series(res['x'], index=df_.index.get_level_values(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import NonlinearConstraint\n",
    "def get_optimization_weight(df: pd.DataFrame):\n",
    "    '''\n",
    "    权重优化\n",
    "    '''\n",
    "    df_ = df.copy()\n",
    "    index_weight = df_['weight'].values\n",
    "    score = df_['SCORE'].values\n",
    "    f = df_.drop(columns=['SCORE', 'weight']).T.values\n",
    "\n",
    "    length = len(df_)\n",
    "    \n",
    "    # 目标函数\n",
    "    func = lambda w: - w.dot(score)\n",
    "\n",
    "    # 约束条件1,2\n",
    "    cons_1 = lambda w:f @ (index_weight -  w)\n",
    "    \n",
    "    # 约束条件3:个股相对于基准指数成分股的偏离\n",
    "    cons_2 = lambda w:w - index_weight\n",
    "    \n",
    "    # 约束条件6:权重之和为1\n",
    "    cons_3 = {'type': 'eq', 'fun': lambda w: np.sum(w) - 1}\n",
    "    \n",
    "    nonlinear_constraint1 = NonlinearConstraint(cons_1,0.95,1.05)\n",
    "    nonlinear_constraint2 = NonlinearConstraint(cons_2,0.,0.05)\n",
    "    \n",
    "    # 建立约束条件5，个股权重上限，及权重的取值范围\n",
    "    limit = tuple((0, 1.5) for x in range(length))\n",
    "\n",
    "    # 利用递归最小二乘求解最优权重\n",
    "    res = minimize(func, index_weight, method='SLSQP', bounds=limit,\n",
    "                   constraints=[nonlinear_constraint1,nonlinear_constraint2,cons_3])  \n",
    "    \n",
    "    if res['success'] != True:\n",
    "        print(df.name)\n",
    "\n",
    "    return pd.Series(res['x'], index=df_.index.get_level_values(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_df = factors5.loc['2010-01-29'].copy()\n",
    "\n",
    "slice_df['weight'] = get_index_weights(\n",
    "    '000300.XSHG', '2010-01-29').set_index('date', append=True).swaplevel()['weight']\n",
    "\n",
    "select_col = [col for col in slice_df.columns if col not in ['market_cap','NEXT_RET']]\n",
    "\n",
    "df = pd.get_dummies(slice_df[select_col],columns=['INDUSTRY_CODE'])\n",
    "    \n",
    "index_weight = df['weight'].values\n",
    "score = df['SCORE'].values\n",
    "f = df.drop(columns=['SCORE', 'weight']).T.values\n",
    "\n",
    "length = len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimization_result(symbol:str,start:str,end:str,factors:pd.DataFrame)->pd.DataFrame:\n",
    "    '''\n",
    "    获取优化权重后的结果\n",
    "    '''\n",
    "    factors = factors.copy()\n",
    "    ind_weight = get_weighs(symbol, start, end)\n",
    "    factors['weight'] = ind_weight['weight']\n",
    "\n",
    "\n",
    "    index_weight = factors['weight'].values\n",
    "    select_col = [col for col in factors.columns if col not in ['market_cap','NEXT_RET']]\n",
    "\n",
    "    df = pd.get_dummies(factors[select_col],columns=['INDUSTRY_CODE'])\n",
    "    \n",
    "    result_df = df.groupby(level='date').apply(get_optimization_weight)\n",
    "    \n",
    "    return result_df.to_frame('w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到大部分日期没有计算得到最优解,但迭代计算的结果也会优于原始权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-29 00:00:00\n",
      "2010-02-26 00:00:00\n",
      "2010-03-31 00:00:00\n",
      "2010-04-30 00:00:00\n",
      "2010-05-31 00:00:00\n",
      "2010-06-30 00:00:00\n",
      "2010-07-30 00:00:00\n",
      "2010-08-31 00:00:00\n",
      "2010-09-30 00:00:00\n",
      "2010-10-29 00:00:00\n",
      "2010-11-30 00:00:00\n",
      "2010-12-31 00:00:00\n",
      "2011-01-31 00:00:00\n",
      "2011-02-28 00:00:00\n",
      "2011-03-31 00:00:00\n",
      "2011-04-29 00:00:00\n",
      "2011-05-31 00:00:00\n",
      "2011-06-30 00:00:00\n",
      "2011-07-29 00:00:00\n",
      "2011-08-31 00:00:00\n",
      "2011-09-30 00:00:00\n",
      "2011-10-31 00:00:00\n",
      "2011-11-30 00:00:00\n",
      "2011-12-30 00:00:00\n",
      "2012-01-31 00:00:00\n",
      "2012-02-29 00:00:00\n",
      "2012-03-30 00:00:00\n",
      "2012-04-27 00:00:00\n",
      "2012-05-31 00:00:00\n",
      "2012-06-29 00:00:00\n",
      "2012-07-31 00:00:00\n",
      "2012-08-31 00:00:00\n",
      "2012-09-28 00:00:00\n",
      "2012-10-31 00:00:00\n",
      "2012-11-30 00:00:00\n",
      "2012-12-31 00:00:00\n",
      "2013-01-31 00:00:00\n",
      "2013-02-28 00:00:00\n",
      "2013-03-29 00:00:00\n",
      "2013-04-26 00:00:00\n",
      "2013-05-31 00:00:00\n",
      "2013-06-28 00:00:00\n",
      "2013-07-31 00:00:00\n",
      "2013-08-30 00:00:00\n",
      "2013-09-30 00:00:00\n",
      "2013-10-31 00:00:00\n",
      "2013-11-29 00:00:00\n",
      "2013-12-31 00:00:00\n",
      "2014-01-30 00:00:00\n",
      "2014-02-28 00:00:00\n",
      "2014-03-31 00:00:00\n",
      "2014-04-30 00:00:00\n",
      "2014-05-30 00:00:00\n",
      "2014-06-30 00:00:00\n",
      "2014-07-31 00:00:00\n",
      "2014-08-29 00:00:00\n",
      "2014-09-30 00:00:00\n",
      "2014-10-31 00:00:00\n",
      "2014-11-28 00:00:00\n",
      "2014-12-31 00:00:00\n",
      "2015-01-30 00:00:00\n",
      "2015-02-27 00:00:00\n",
      "2015-03-31 00:00:00\n",
      "2015-04-30 00:00:00\n",
      "2015-05-29 00:00:00\n",
      "2015-06-30 00:00:00\n",
      "2015-07-31 00:00:00\n",
      "2015-08-31 00:00:00\n",
      "2015-09-30 00:00:00\n",
      "2015-10-30 00:00:00\n",
      "2015-11-30 00:00:00\n",
      "2015-12-31 00:00:00\n",
      "2016-01-29 00:00:00\n",
      "2016-02-29 00:00:00\n",
      "2016-03-31 00:00:00\n",
      "2016-04-29 00:00:00\n",
      "2016-05-31 00:00:00\n",
      "2016-06-30 00:00:00\n",
      "2016-07-29 00:00:00\n",
      "2016-08-31 00:00:00\n",
      "2016-09-30 00:00:00\n",
      "2016-10-31 00:00:00\n",
      "2016-11-30 00:00:00\n",
      "2016-12-30 00:00:00\n",
      "2017-01-26 00:00:00\n",
      "2017-02-28 00:00:00\n",
      "2017-03-31 00:00:00\n",
      "2017-04-28 00:00:00\n",
      "2017-05-31 00:00:00\n",
      "2017-06-30 00:00:00\n",
      "2017-07-31 00:00:00\n",
      "2017-08-31 00:00:00\n",
      "2017-09-29 00:00:00\n",
      "2017-10-31 00:00:00\n",
      "2017-11-30 00:00:00\n",
      "2017-12-29 00:00:00\n",
      "2018-01-31 00:00:00\n",
      "2018-02-28 00:00:00\n",
      "2018-03-30 00:00:00\n",
      "2018-04-27 00:00:00\n",
      "2018-05-31 00:00:00\n",
      "2018-06-29 00:00:00\n",
      "2018-07-31 00:00:00\n",
      "2018-08-31 00:00:00\n",
      "2018-09-28 00:00:00\n",
      "2018-10-31 00:00:00\n",
      "2018-11-30 00:00:00\n",
      "2018-12-28 00:00:00\n",
      "2019-01-31 00:00:00\n",
      "2019-02-28 00:00:00\n",
      "2019-03-29 00:00:00\n",
      "2019-04-30 00:00:00\n",
      "2019-05-31 00:00:00\n",
      "2019-06-28 00:00:00\n",
      "2019-07-31 00:00:00\n",
      "2019-08-30 00:00:00\n",
      "2019-09-30 00:00:00\n",
      "2019-10-31 00:00:00\n",
      "2019-11-29 00:00:00\n",
      "2019-12-31 00:00:00\n",
      "2020-01-23 00:00:00\n",
      "2020-02-28 00:00:00\n",
      "2020-03-31 00:00:00\n",
      "2020-04-30 00:00:00\n",
      "2020-05-29 00:00:00\n",
      "2020-06-30 00:00:00\n",
      "2020-07-31 00:00:00\n",
      "2020-08-31 00:00:00\n",
      "2020-09-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "result1_df = optimization_result('000300.XSHG',START_DATE,END_DATE,factors5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-29</th>\n",
       "      <th>000001.XSHE</th>\n",
       "      <td>0.0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.XSHE</th>\n",
       "      <td>0.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.XSHE</th>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000012.XSHE</th>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000021.XSHE</th>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             w\n",
       "date       code               \n",
       "2010-01-29 000001.XSHE  0.0131\n",
       "           000002.XSHE  0.0176\n",
       "           000009.XSHE  0.0022\n",
       "           000012.XSHE  0.0016\n",
       "           000021.XSHE  0.0011"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 储存结构用于回测\n",
    "result1_df.to_csv('../../result1_df.csv')\n",
    "result1_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>benchmark_returns</th>\n",
       "      <th>returns</th>\n",
       "      <th>excess_returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04 16:00:00</th>\n",
       "      <td>-0.011313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05 16:00:00</th>\n",
       "      <td>-0.003255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06 16:00:00</th>\n",
       "      <td>-0.009495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07 16:00:00</th>\n",
       "      <td>-0.029147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08 16:00:00</th>\n",
       "      <td>-0.026722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     benchmark_returns  returns  excess_returns\n",
       "time                                                           \n",
       "2010-01-04 16:00:00          -0.011313      0.0        0.011442\n",
       "2010-01-05 16:00:00          -0.003255      0.0        0.003266\n",
       "2010-01-06 16:00:00          -0.009495      0.0        0.009586\n",
       "2010-01-07 16:00:00          -0.029147      0.0        0.030022\n",
       "2010-01-08 16:00:00          -0.026722      0.0        0.027456"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据result_df文件中生成的权重及股票名单进行回测\n",
    "# 获取回测结果\n",
    "gt = get_backtest('477d928643e935c2cdc18f24bed2c9c0')\n",
    "\n",
    "algorithm = pd.DataFrame(gt.get_results()).set_index('time')\n",
    "algorithm['excess_returns'] = (\n",
    "    algorithm['returns'] + 1) / (algorithm['benchmark_returns'] + 1) - 1\n",
    "\n",
    "algorithm.index = pd.to_datetime(algorithm.index)\n",
    "algorithm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6078ed2da0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = 'serif'\n",
    "algorithm.plot.line(figsize=(18, 6), title='分层抽样指数增强策略', color=[\n",
    "                    'LightGrey', 'LightCoral', 'GoldenRod'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "algorithm_return                                     0.745218\n",
       "algorithm_volatility                                 0.207377\n",
       "alpha                                               0.0248952\n",
       "annual_algo_return                                  0.0545104\n",
       "annual_bm_return                                    0.0283163\n",
       "avg_excess_return                                 0.000103468\n",
       "avg_position_days                                     1105.51\n",
       "avg_trade_return                                     0.121754\n",
       "benchmark_return                                     0.340397\n",
       "benchmark_volatility                                 0.231306\n",
       "beta                                                 0.888823\n",
       "day_win_ratio                                        0.495616\n",
       "excess_return                                        0.302016\n",
       "excess_return_max_drawdown                          0.0905644\n",
       "excess_return_max_drawdown_period    [2010-07-05, 2010-11-10]\n",
       "excess_return_sharpe                                -0.383973\n",
       "information                                          0.700219\n",
       "lose_count                                                665\n",
       "max_drawdown                                         0.416183\n",
       "max_drawdown_period                  [2015-06-08, 2016-01-28]\n",
       "max_leverage                                                0\n",
       "period_label                                          2020-10\n",
       "profit_loss_ratio                                     1.46004\n",
       "sharpe                                              0.0699709\n",
       "sortino                                             0.0893679\n",
       "trading_days                                             2623\n",
       "treasury_return                                       0.43211\n",
       "turnover_rate                                      0.00079364\n",
       "win_count                                                 550\n",
       "win_ratio                                            0.452675\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 风险指标如下\n",
    "pd.Series(gt.get_risk()).iloc[1:]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "MarkDown菜单",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
